Overview

Brought to you by YData

Dataset statistics

Number of variables151
Number of observations2260701
Missing cells108486252
Missing cells (%)31.8%
Total size in memory5.8 GiB
Average record size in memory2.7 KiB

Variable types

Unsupported2
Numeric112
Text36
URL1

Alerts

policy_code has constant value "1.0" Constant
hardship_type has constant value "INTEREST ONLY-3 MONTHS DEFERRAL" Constant
deferral_term has constant value "3.0" Constant
hardship_length has constant value "3.0" Constant
member_id has 2260701 (100.0%) missing values Missing
emp_title has 167002 (7.4%) missing values Missing
emp_length has 146940 (6.5%) missing values Missing
desc has 2134636 (94.4%) missing values Missing
title has 23359 (1.0%) missing values Missing
mths_since_last_delinq has 1158535 (51.2%) missing values Missing
mths_since_last_record has 1901545 (84.1%) missing values Missing
next_pymnt_d has 1345343 (59.5%) missing values Missing
mths_since_last_major_derog has 1679926 (74.3%) missing values Missing
annual_inc_joint has 2139991 (94.7%) missing values Missing
dti_joint has 2139995 (94.7%) missing values Missing
verification_status_joint has 2144971 (94.9%) missing values Missing
tot_coll_amt has 70309 (3.1%) missing values Missing
tot_cur_bal has 70309 (3.1%) missing values Missing
open_acc_6m has 866163 (38.3%) missing values Missing
open_act_il has 866162 (38.3%) missing values Missing
open_il_12m has 866162 (38.3%) missing values Missing
open_il_24m has 866162 (38.3%) missing values Missing
mths_since_rcnt_il has 909957 (40.3%) missing values Missing
total_bal_il has 866162 (38.3%) missing values Missing
il_util has 1068883 (47.3%) missing values Missing
open_rv_12m has 866162 (38.3%) missing values Missing
open_rv_24m has 866162 (38.3%) missing values Missing
max_bal_bc has 866162 (38.3%) missing values Missing
all_util has 866381 (38.3%) missing values Missing
total_rev_hi_lim has 70309 (3.1%) missing values Missing
inq_fi has 866162 (38.3%) missing values Missing
total_cu_tl has 866163 (38.3%) missing values Missing
inq_last_12m has 866163 (38.3%) missing values Missing
acc_open_past_24mths has 50063 (2.2%) missing values Missing
avg_cur_bal has 70379 (3.1%) missing values Missing
bc_open_to_buy has 74968 (3.3%) missing values Missing
bc_util has 76104 (3.4%) missing values Missing
mo_sin_old_il_acct has 139104 (6.2%) missing values Missing
mo_sin_old_rev_tl_op has 70310 (3.1%) missing values Missing
mo_sin_rcnt_rev_tl_op has 70310 (3.1%) missing values Missing
mo_sin_rcnt_tl has 70309 (3.1%) missing values Missing
mort_acc has 50063 (2.2%) missing values Missing
mths_since_recent_bc has 73445 (3.2%) missing values Missing
mths_since_recent_bc_dlq has 1741000 (77.0%) missing values Missing
mths_since_recent_inq has 295468 (13.1%) missing values Missing
mths_since_recent_revol_delinq has 1520342 (67.3%) missing values Missing
num_accts_ever_120_pd has 70309 (3.1%) missing values Missing
num_actv_bc_tl has 70309 (3.1%) missing values Missing
num_actv_rev_tl has 70309 (3.1%) missing values Missing
num_bc_sats has 58623 (2.6%) missing values Missing
num_bc_tl has 70309 (3.1%) missing values Missing
num_il_tl has 70309 (3.1%) missing values Missing
num_op_rev_tl has 70309 (3.1%) missing values Missing
num_rev_accts has 70310 (3.1%) missing values Missing
num_rev_tl_bal_gt_0 has 70309 (3.1%) missing values Missing
num_sats has 58623 (2.6%) missing values Missing
num_tl_120dpd_2m has 153690 (6.8%) missing values Missing
num_tl_30dpd has 70309 (3.1%) missing values Missing
num_tl_90g_dpd_24m has 70309 (3.1%) missing values Missing
num_tl_op_past_12m has 70309 (3.1%) missing values Missing
pct_tl_nvr_dlq has 70464 (3.1%) missing values Missing
percent_bc_gt_75 has 75412 (3.3%) missing values Missing
tot_hi_cred_lim has 70309 (3.1%) missing values Missing
total_bal_ex_mort has 50063 (2.2%) missing values Missing
total_bc_limit has 50063 (2.2%) missing values Missing
total_il_high_credit_limit has 70309 (3.1%) missing values Missing
revol_bal_joint has 2152681 (95.2%) missing values Missing
sec_app_fico_range_low has 2152680 (95.2%) missing values Missing
sec_app_fico_range_high has 2152680 (95.2%) missing values Missing
sec_app_earliest_cr_line has 2152680 (95.2%) missing values Missing
sec_app_inq_last_6mths has 2152680 (95.2%) missing values Missing
sec_app_mort_acc has 2152680 (95.2%) missing values Missing
sec_app_open_acc has 2152680 (95.2%) missing values Missing
sec_app_revol_util has 2154517 (95.3%) missing values Missing
sec_app_open_act_il has 2152680 (95.2%) missing values Missing
sec_app_num_rev_accts has 2152680 (95.2%) missing values Missing
sec_app_chargeoff_within_12_mths has 2152680 (95.2%) missing values Missing
sec_app_collections_12_mths_ex_med has 2152680 (95.2%) missing values Missing
sec_app_mths_since_last_major_derog has 2224759 (98.4%) missing values Missing
hardship_type has 2249784 (99.5%) missing values Missing
hardship_reason has 2249784 (99.5%) missing values Missing
hardship_status has 2249784 (99.5%) missing values Missing
deferral_term has 2249784 (99.5%) missing values Missing
hardship_amount has 2249784 (99.5%) missing values Missing
hardship_start_date has 2249784 (99.5%) missing values Missing
hardship_end_date has 2249784 (99.5%) missing values Missing
payment_plan_start_date has 2249784 (99.5%) missing values Missing
hardship_length has 2249784 (99.5%) missing values Missing
hardship_dpd has 2249784 (99.5%) missing values Missing
hardship_loan_status has 2249784 (99.5%) missing values Missing
orig_projected_additional_accrued_interest has 2252050 (99.6%) missing values Missing
hardship_payoff_balance_amount has 2249784 (99.5%) missing values Missing
hardship_last_payment_amount has 2249784 (99.5%) missing values Missing
debt_settlement_flag_date has 2226455 (98.5%) missing values Missing
settlement_status has 2226455 (98.5%) missing values Missing
settlement_date has 2226455 (98.5%) missing values Missing
settlement_amount has 2226455 (98.5%) missing values Missing
settlement_percentage has 2226455 (98.5%) missing values Missing
settlement_term has 2226455 (98.5%) missing values Missing
annual_inc is highly skewed (γ1 = 493.8860884) Skewed
dti is highly skewed (γ1 = 29.20185447) Skewed
total_rec_late_fee is highly skewed (γ1 = 21.90352305) Skewed
annual_inc_joint is highly skewed (γ1 = 21.7445355) Skewed
acc_now_delinq is highly skewed (γ1 = 22.90797767) Skewed
tot_coll_amt is highly skewed (γ1 = 852.0101323) Skewed
total_rev_hi_lim is highly skewed (γ1 = 32.55742738) Skewed
delinq_amnt is highly skewed (γ1 = 102.6547743) Skewed
num_tl_120dpd_2m is highly skewed (γ1 = 55.80984712) Skewed
num_tl_30dpd is highly skewed (γ1 = 22.51746312) Skewed
tax_liens is highly skewed (γ1 = 32.07091145) Skewed
sec_app_chargeoff_within_12_mths is highly skewed (γ1 = 20.27699345) Skewed
id is an unsupported type, check if it needs cleaning or further analysis Unsupported
member_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
delinq_2yrs has 1839108 (81.4%) zeros Zeros
inq_last_6mths has 1381722 (61.1%) zeros Zeros
pub_rec has 1902758 (84.2%) zeros Zeros
out_prncp has 1352764 (59.8%) zeros Zeros
out_prncp_inv has 1352764 (59.8%) zeros Zeros
total_rec_late_fee has 2173513 (96.1%) zeros Zeros
recoveries has 2075236 (91.8%) zeros Zeros
collection_recovery_fee has 2083655 (92.2%) zeros Zeros
last_fico_range_low has 37326 (1.7%) zeros Zeros
collections_12_mths_ex_med has 2223085 (98.3%) zeros Zeros
acc_now_delinq has 2251857 (99.6%) zeros Zeros
tot_coll_amt has 1856129 (82.1%) zeros Zeros
open_acc_6m has 627966 (27.8%) zeros Zeros
open_act_il has 165848 (7.3%) zeros Zeros
open_il_12m has 760254 (33.6%) zeros Zeros
open_il_24m has 377489 (16.7%) zeros Zeros
total_bal_il has 158666 (7.0%) zeros Zeros
open_rv_12m has 513716 (22.7%) zeros Zeros
open_rv_24m has 223783 (9.9%) zeros Zeros
max_bal_bc has 35917 (1.6%) zeros Zeros
inq_fi has 697142 (30.8%) zeros Zeros
total_cu_tl has 753128 (33.3%) zeros Zeros
inq_last_12m has 400090 (17.7%) zeros Zeros
acc_open_past_24mths has 100270 (4.4%) zeros Zeros
bc_open_to_buy has 30767 (1.4%) zeros Zeros
bc_util has 27885 (1.2%) zeros Zeros
chargeoff_within_12_mths has 2243339 (99.2%) zeros Zeros
delinq_amnt has 2253465 (99.7%) zeros Zeros
mo_sin_rcnt_rev_tl_op has 33747 (1.5%) zeros Zeros
mo_sin_rcnt_tl has 34923 (1.5%) zeros Zeros
mort_acc has 929606 (41.1%) zeros Zeros
mths_since_recent_inq has 168927 (7.5%) zeros Zeros
num_accts_ever_120_pd has 1687416 (74.6%) zeros Zeros
num_actv_bc_tl has 50061 (2.2%) zeros Zeros
num_bc_sats has 23661 (1.0%) zeros Zeros
num_il_tl has 68944 (3.0%) zeros Zeros
num_tl_120dpd_2m has 2105738 (93.1%) zeros Zeros
num_tl_30dpd has 2184561 (96.6%) zeros Zeros
num_tl_90g_dpd_24m has 2073060 (91.7%) zeros Zeros
num_tl_op_past_12m has 415975 (18.4%) zeros Zeros
percent_bc_gt_75 has 598711 (26.5%) zeros Zeros
pub_rec_bankruptcies has 1987383 (87.9%) zeros Zeros
tax_liens has 2195933 (97.1%) zeros Zeros
total_bc_limit has 25349 (1.1%) zeros Zeros
total_il_high_credit_limit has 263497 (11.7%) zeros Zeros
sec_app_inq_last_6mths has 65252 (2.9%) zeros Zeros
sec_app_mort_acc has 42218 (1.9%) zeros Zeros
sec_app_chargeoff_within_12_mths has 105117 (4.6%) zeros Zeros
sec_app_collections_12_mths_ex_med has 101793 (4.5%) zeros Zeros

Reproduction

Analysis started2025-07-26 11:58:35.314930
Analysis finished2025-07-26 12:00:32.049626
Duration1 minute and 56.73 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size78.9 MiB

member_id
Unsupported

Missing  Rejected  Unsupported 

Missing2260701
Missing (%)100.0%
Memory size17.2 MiB

loan_amnt
Real number (ℝ)

Distinct1572
Distinct (%)0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15046.93123
Minimum500
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:32.195081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile3250
Q18000
median12900
Q320000
95-th percentile35000
Maximum40000
Range39500
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation9190.245488
Coefficient of variation (CV)0.6107720803
Kurtosis-0.1194391577
Mean15046.93123
Median Absolute Deviation (MAD)6200
Skewness0.7777823287
Sum3.401611592 × 1010
Variance84460612.13
MonotonicityNot monotonic
2025-07-26T13:00:32.326849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 187236
 
8.3%
20000 131006
 
5.8%
15000 123226
 
5.5%
12000 121681
 
5.4%
35000 86285
 
3.8%
5000 84765
 
3.7%
8000 75033
 
3.3%
6000 72089
 
3.2%
25000 66453
 
2.9%
16000 66418
 
2.9%
Other values (1562) 1246476
55.1%
ValueCountFrequency (%)
500 11
< 0.1%
550 1
 
< 0.1%
600 6
< 0.1%
700 3
 
< 0.1%
725 1
 
< 0.1%
ValueCountFrequency (%)
40000 33368
1.5%
39975 11
 
< 0.1%
39950 10
 
< 0.1%
39925 14
 
< 0.1%
39900 24
 
< 0.1%

funded_amnt
Real number (ℝ)

Distinct1572
Distinct (%)0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15041.66406
Minimum500
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:33.051604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile3250
Q18000
median12875
Q320000
95-th percentile35000
Maximum40000
Range39500
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation9188.413022
Coefficient of variation (CV)0.6108641296
Kurtosis-0.1170090387
Mean15041.66406
Median Absolute Deviation (MAD)6175
Skewness0.7787785936
Sum3.40042086 × 1010
Variance84426933.87
MonotonicityNot monotonic
2025-07-26T13:00:33.110322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 187146
 
8.3%
20000 130816
 
5.8%
15000 123110
 
5.4%
12000 121588
 
5.4%
35000 86147
 
3.8%
5000 84751
 
3.7%
8000 75020
 
3.3%
6000 72075
 
3.2%
16000 66331
 
2.9%
25000 66176
 
2.9%
Other values (1562) 1247508
55.2%
ValueCountFrequency (%)
500 11
< 0.1%
550 1
 
< 0.1%
600 6
< 0.1%
700 3
 
< 0.1%
725 1
 
< 0.1%
ValueCountFrequency (%)
40000 33368
1.5%
39975 11
 
< 0.1%
39950 10
 
< 0.1%
39925 14
 
< 0.1%
39900 24
 
< 0.1%

funded_amnt_inv
Real number (ℝ)

Distinct10057
Distinct (%)0.4%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15023.43775
Minimum0
Maximum40000
Zeros233
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:33.221529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3200
Q18000
median12800
Q320000
95-th percentile35000
Maximum40000
Range40000
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation9192.331679
Coefficient of variation (CV)0.6118660612
Kurtosis-0.1166814573
Mean15023.43775
Median Absolute Deviation (MAD)6200
Skewness0.7782542751
Sum3.396300496 × 1010
Variance84498961.69
MonotonicityNot monotonic
2025-07-26T13:00:33.272023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 177561
 
7.9%
20000 120453
 
5.3%
15000 114539
 
5.1%
12000 114068
 
5.0%
5000 81999
 
3.6%
35000 76093
 
3.4%
8000 71528
 
3.2%
6000 69475
 
3.1%
16000 61840
 
2.7%
25000 60610
 
2.7%
Other values (10047) 1312502
58.1%
ValueCountFrequency (%)
0 233
< 0.1%
0.000121098108 1
 
< 0.1%
0.000185369401 1
 
< 0.1%
0.000242055511 1
 
< 0.1%
0.000531133069 1
 
< 0.1%
ValueCountFrequency (%)
40000 31767
1.4%
39975 616
 
< 0.1%
39950 218
 
< 0.1%
39925 58
 
< 0.1%
39900 29
 
< 0.1%

term
Text

Distinct2
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size127.2 MiB
2025-07-26T13:00:33.325678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters22606680
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 36 months
3rd row 60 months
4th row 60 months
5th row 60 months
ValueCountFrequency (%)
months 2260668
50.0%
36 1609754
35.6%
60 650914
 
14.4%
2025-07-26T13:00:33.407795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4521336
20.0%
6 2260668
10.0%
m 2260668
10.0%
o 2260668
10.0%
n 2260668
10.0%
t 2260668
10.0%
h 2260668
10.0%
s 2260668
10.0%
3 1609754
 
7.1%
0 650914
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22606680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4521336
20.0%
6 2260668
10.0%
m 2260668
10.0%
o 2260668
10.0%
n 2260668
10.0%
t 2260668
10.0%
h 2260668
10.0%
s 2260668
10.0%
3 1609754
 
7.1%
0 650914
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22606680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4521336
20.0%
6 2260668
10.0%
m 2260668
10.0%
o 2260668
10.0%
n 2260668
10.0%
t 2260668
10.0%
h 2260668
10.0%
s 2260668
10.0%
3 1609754
 
7.1%
0 650914
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22606680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4521336
20.0%
6 2260668
10.0%
m 2260668
10.0%
o 2260668
10.0%
n 2260668
10.0%
t 2260668
10.0%
h 2260668
10.0%
s 2260668
10.0%
3 1609754
 
7.1%
0 650914
 
2.9%

int_rate
Real number (ℝ)

Distinct673
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13.09282912
Minimum5.31
Maximum30.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:33.450966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.31
5-th percentile6.49
Q19.49
median12.62
Q315.99
95-th percentile22.15
Maximum30.99
Range25.68
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation4.832138365
Coefficient of variation (CV)0.36906755
Kurtosis0.5940245697
Mean13.09282912
Median Absolute Deviation (MAD)3.18
Skewness0.7680705625
Sum29598539.81
Variance23.34956117
MonotonicityNot monotonic
2025-07-26T13:00:33.498889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.99 53869
 
2.4%
5.32 47171
 
2.1%
10.99 44165
 
2.0%
13.99 43025
 
1.9%
11.49 32010
 
1.4%
16.99 30564
 
1.4%
12.99 29276
 
1.3%
7.89 28514
 
1.3%
9.17 27835
 
1.2%
15.61 25208
 
1.1%
Other values (663) 1899031
84.0%
ValueCountFrequency (%)
5.31 8613
 
0.4%
5.32 47171
2.1%
5.42 573
 
< 0.1%
5.79 410
 
< 0.1%
5.93 1812
 
0.1%
ValueCountFrequency (%)
30.99 819
< 0.1%
30.94 733
< 0.1%
30.89 699
< 0.1%
30.84 755
< 0.1%
30.79 1572
0.1%

installment
Real number (ℝ)

Distinct93301
Distinct (%)4.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean445.8068229
Minimum4.93
Maximum1719.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:33.546049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.93
5-th percentile110.43
Q1251.65
median377.99
Q3593.32
95-th percentile984.47
Maximum1719.83
Range1714.9
Interquartile range (IQR)341.67

Descriptive statistics

Standard deviation267.1735346
Coefficient of variation (CV)0.5993033774
Kurtosis0.6898790426
Mean445.8068229
Median Absolute Deviation (MAD)157.63
Skewness1.001780569
Sum1007821219
Variance71381.6976
MonotonicityNot monotonic
2025-07-26T13:00:33.593469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301.15 4420
 
0.2%
332.1 4153
 
0.2%
361.38 3704
 
0.2%
327.34 3353
 
0.1%
602.3 3095
 
0.1%
451.73 3076
 
0.1%
329.72 2614
 
0.1%
166.05 2508
 
0.1%
498.15 2410
 
0.1%
180.69 2364
 
0.1%
Other values (93291) 2228971
98.6%
ValueCountFrequency (%)
4.93 1
< 0.1%
7.61 1
< 0.1%
14.01 1
< 0.1%
14.77 1
< 0.1%
15.67 1
< 0.1%
ValueCountFrequency (%)
1719.83 2
 
< 0.1%
1717.63 1
 
< 0.1%
1715.42 2
 
< 0.1%
1714.54 6
< 0.1%
1691.28 2
 
< 0.1%

grade
Text

Distinct7
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size107.8 MiB
2025-07-26T13:00:33.635012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2260668
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowB
4th rowC
5th rowF
ValueCountFrequency (%)
b 663557
29.4%
c 650053
28.8%
a 433027
19.2%
d 324424
14.4%
e 135639
 
6.0%
f 41800
 
1.8%
g 12168
 
0.5%
2025-07-26T13:00:33.702494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 663557
29.4%
C 650053
28.8%
A 433027
19.2%
D 324424
14.4%
E 135639
 
6.0%
F 41800
 
1.8%
G 12168
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 663557
29.4%
C 650053
28.8%
A 433027
19.2%
D 324424
14.4%
E 135639
 
6.0%
F 41800
 
1.8%
G 12168
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 663557
29.4%
C 650053
28.8%
A 433027
19.2%
D 324424
14.4%
E 135639
 
6.0%
F 41800
 
1.8%
G 12168
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 663557
29.4%
C 650053
28.8%
A 433027
19.2%
D 324424
14.4%
E 135639
 
6.0%
F 41800
 
1.8%
G 12168
 
0.5%
Distinct35
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size110.0 MiB
2025-07-26T13:00:33.760881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4521336
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC4
2nd rowC1
3rd rowB4
4th rowC5
5th rowF1
ValueCountFrequency (%)
c1 145903
 
6.5%
b5 140288
 
6.2%
b4 139793
 
6.2%
b3 131514
 
5.8%
c2 131116
 
5.8%
c3 129193
 
5.7%
c4 127115
 
5.6%
b2 126621
 
5.6%
b1 125341
 
5.5%
c5 116726
 
5.2%
Other values (25) 947058
41.9%
2025-07-26T13:00:33.877066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 663557
14.7%
C 650053
14.4%
1 490913
10.9%
4 450277
10.0%
2 442115
9.8%
5 442060
9.8%
3 435303
9.6%
A 433027
9.6%
D 324424
7.2%
E 135639
 
3.0%
Other values (2) 53968
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4521336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 663557
14.7%
C 650053
14.4%
1 490913
10.9%
4 450277
10.0%
2 442115
9.8%
5 442060
9.8%
3 435303
9.6%
A 433027
9.6%
D 324424
7.2%
E 135639
 
3.0%
Other values (2) 53968
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4521336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 663557
14.7%
C 650053
14.4%
1 490913
10.9%
4 450277
10.0%
2 442115
9.8%
5 442060
9.8%
3 435303
9.6%
A 433027
9.6%
D 324424
7.2%
E 135639
 
3.0%
Other values (2) 53968
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4521336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 663557
14.7%
C 650053
14.4%
1 490913
10.9%
4 450277
10.0%
2 442115
9.8%
5 442060
9.8%
3 435303
9.6%
A 433027
9.6%
D 324424
7.2%
E 135639
 
3.0%
Other values (2) 53968
 
1.2%

emp_title
Text

Missing 

Distinct512694
Distinct (%)24.5%
Missing167002
Missing (%)7.4%
Memory size134.1 MiB
2025-07-26T13:00:34.145077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length78
Median length57
Mean length15.59612771
Min length1

Characters and Unicode

Total characters32653597
Distinct characters170
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique387287 ?
Unique (%)18.5%

Sample

1st rowleadman
2nd rowEngineer
3rd rowtruck driver
4th rowInformation Systems Officer
5th rowContract Specialist
ValueCountFrequency (%)
manager 305625
 
7.2%
director 80439
 
1.9%
assistant 72742
 
1.7%
sales 72204
 
1.7%
supervisor 58337
 
1.4%
teacher 57228
 
1.3%
specialist 55010
 
1.3%
of 54332
 
1.3%
senior 52896
 
1.2%
engineer 52508
 
1.2%
Other values (101705) 3406104
79.8%
2025-07-26T13:00:34.392381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3491625
 
10.7%
r 2802534
 
8.6%
a 2538574
 
7.8%
2377318
 
7.3%
i 2248840
 
6.9%
n 2200411
 
6.7%
t 2010915
 
6.2%
o 1622361
 
5.0%
s 1581963
 
4.8%
c 1365755
 
4.2%
Other values (160) 10413301
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32653597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3491625
 
10.7%
r 2802534
 
8.6%
a 2538574
 
7.8%
2377318
 
7.3%
i 2248840
 
6.9%
n 2200411
 
6.7%
t 2010915
 
6.2%
o 1622361
 
5.0%
s 1581963
 
4.8%
c 1365755
 
4.2%
Other values (160) 10413301
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32653597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3491625
 
10.7%
r 2802534
 
8.6%
a 2538574
 
7.8%
2377318
 
7.3%
i 2248840
 
6.9%
n 2200411
 
6.7%
t 2010915
 
6.2%
o 1622361
 
5.0%
s 1581963
 
4.8%
c 1365755
 
4.2%
Other values (160) 10413301
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32653597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3491625
 
10.7%
r 2802534
 
8.6%
a 2538574
 
7.8%
2377318
 
7.3%
i 2248840
 
6.9%
n 2200411
 
6.7%
t 2010915
 
6.2%
o 1622361
 
5.0%
s 1581963
 
4.8%
c 1365755
 
4.2%
Other values (160) 10413301
31.9%

emp_length
Text

Missing 

Distinct11
Distinct (%)< 0.1%
Missing146940
Missing (%)6.5%
Memory size118.8 MiB
2025-07-26T13:00:34.444037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.727421407
Min length6

Characters and Unicode

Total characters16333922
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row10+ years
3rd row10+ years
4th row10+ years
5th row3 years
ValueCountFrequency (%)
years 1775370
40.2%
10 748005
16.9%
1 338391
 
7.7%
year 338391
 
7.7%
2 203677
 
4.6%
189988
 
4.3%
3 180753
 
4.1%
5 139698
 
3.2%
4 136605
 
3.1%
6 102628
 
2.3%
Other values (3) 264004
 
6.0%
2025-07-26T13:00:34.530975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2303749
14.1%
y 2113761
12.9%
e 2113761
12.9%
a 2113761
12.9%
r 2113761
12.9%
s 1775370
10.9%
1 1086396
6.7%
0 748005
 
4.6%
+ 748005
 
4.6%
2 203677
 
1.2%
Other values (8) 1013676
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16333922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2303749
14.1%
y 2113761
12.9%
e 2113761
12.9%
a 2113761
12.9%
r 2113761
12.9%
s 1775370
10.9%
1 1086396
6.7%
0 748005
 
4.6%
+ 748005
 
4.6%
2 203677
 
1.2%
Other values (8) 1013676
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16333922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2303749
14.1%
y 2113761
12.9%
e 2113761
12.9%
a 2113761
12.9%
r 2113761
12.9%
s 1775370
10.9%
1 1086396
6.7%
0 748005
 
4.6%
+ 748005
 
4.6%
2 203677
 
1.2%
Other values (8) 1013676
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16333922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2303749
14.1%
y 2113761
12.9%
e 2113761
12.9%
a 2113761
12.9%
r 2113761
12.9%
s 1775370
10.9%
1 1086396
6.7%
0 748005
 
4.6%
+ 748005
 
4.6%
2 203677
 
1.2%
Other values (8) 1013676
6.2%
Distinct6
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size118.3 MiB
2025-07-26T13:00:34.581588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.854287759
Min length3

Characters and Unicode

Total characters13234601
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMORTGAGE
2nd rowMORTGAGE
3rd rowMORTGAGE
4th rowMORTGAGE
5th rowMORTGAGE
ValueCountFrequency (%)
mortgage 1111450
49.2%
rent 894929
39.6%
own 253057
 
11.2%
any 996
 
< 0.1%
other 182
 
< 0.1%
none 54
 
< 0.1%
2025-07-26T13:00:34.669754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 2222900
16.8%
E 2006615
15.2%
R 2006561
15.2%
T 2006561
15.2%
O 1364743
10.3%
N 1149090
8.7%
A 1112446
8.4%
M 1111450
8.4%
W 253057
 
1.9%
Y 996
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13234601
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 2222900
16.8%
E 2006615
15.2%
R 2006561
15.2%
T 2006561
15.2%
O 1364743
10.3%
N 1149090
8.7%
A 1112446
8.4%
M 1111450
8.4%
W 253057
 
1.9%
Y 996
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13234601
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 2222900
16.8%
E 2006615
15.2%
R 2006561
15.2%
T 2006561
15.2%
O 1364743
10.3%
N 1149090
8.7%
A 1112446
8.4%
M 1111450
8.4%
W 253057
 
1.9%
Y 996
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13234601
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 2222900
16.8%
E 2006615
15.2%
R 2006561
15.2%
T 2006561
15.2%
O 1364743
10.3%
N 1149090
8.7%
A 1112446
8.4%
M 1111450
8.4%
W 253057
 
1.9%
Y 996
 
< 0.1%

annual_inc
Real number (ℝ)

Skewed 

Distinct89368
Distinct (%)4.0%
Missing37
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean77992.42869
Minimum0
Maximum110000000
Zeros1667
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:34.717738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27600
Q146000
median65000
Q393000
95-th percentile160000
Maximum110000000
Range110000000
Interquartile range (IQR)47000

Descriptive statistics

Standard deviation112696.1996
Coefficient of variation (CV)1.444963331
Kurtosis439001.6589
Mean77992.42869
Median Absolute Deviation (MAD)22000
Skewness493.8860884
Sum1.763146758 × 1011
Variance1.27004334 × 1010
MonotonicityNot monotonic
2025-07-26T13:00:34.768907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 87189
 
3.9%
50000 76355
 
3.4%
65000 64903
 
2.9%
70000 62078
 
2.7%
80000 59833
 
2.6%
40000 59684
 
2.6%
75000 58459
 
2.6%
45000 54534
 
2.4%
55000 51583
 
2.3%
100000 46977
 
2.1%
Other values (89358) 1639069
72.5%
ValueCountFrequency (%)
0 1667
0.1%
0.36 1
 
< 0.1%
1 42
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
110000000 1
< 0.1%
61000000 1
< 0.1%
10999200 1
< 0.1%
9930475 1
< 0.1%
9757200 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size131.6 MiB
2025-07-26T13:00:34.810640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length12
Mean length12.06200335
Min length8

Characters and Unicode

Total characters27268185
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowNot Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified
ValueCountFrequency (%)
verified 2260668
58.1%
source 886231
 
22.8%
not 744806
 
19.1%
2025-07-26T13:00:34.895244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5407567
19.8%
i 4521336
16.6%
r 3146899
11.5%
V 2260668
8.3%
f 2260668
8.3%
d 2260668
8.3%
o 1631037
 
6.0%
1631037
 
6.0%
S 886231
 
3.3%
u 886231
 
3.3%
Other values (3) 2375843
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27268185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5407567
19.8%
i 4521336
16.6%
r 3146899
11.5%
V 2260668
8.3%
f 2260668
8.3%
d 2260668
8.3%
o 1631037
 
6.0%
1631037
 
6.0%
S 886231
 
3.3%
u 886231
 
3.3%
Other values (3) 2375843
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27268185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5407567
19.8%
i 4521336
16.6%
r 3146899
11.5%
V 2260668
8.3%
f 2260668
8.3%
d 2260668
8.3%
o 1631037
 
6.0%
1631037
 
6.0%
S 886231
 
3.3%
u 886231
 
3.3%
Other values (3) 2375843
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27268185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5407567
19.8%
i 4521336
16.6%
r 3146899
11.5%
V 2260668
8.3%
f 2260668
8.3%
d 2260668
8.3%
o 1631037
 
6.0%
1631037
 
6.0%
S 886231
 
3.3%
u 886231
 
3.3%
Other values (3) 2375843
8.7%
Distinct139
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size122.9 MiB
2025-07-26T13:00:35.002592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters18085344
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDec-2015
2nd rowDec-2015
3rd rowDec-2015
4th rowDec-2015
5th rowDec-2015
ValueCountFrequency (%)
mar-2016 61992
 
2.7%
oct-2015 48631
 
2.2%
may-2018 46311
 
2.0%
oct-2018 46305
 
2.0%
aug-2018 46079
 
2.0%
jul-2015 45962
 
2.0%
dec-2015 44343
 
2.0%
aug-2017 43573
 
1.9%
jul-2018 43089
 
1.9%
apr-2018 42928
 
1.9%
Other values (129) 1791455
79.2%
2025-07-26T13:00:35.156051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2314035
 
12.8%
0 2281482
 
12.6%
1 2274112
 
12.6%
- 2260668
 
12.5%
u 591666
 
3.3%
J 552585
 
3.1%
a 537743
 
3.0%
e 515755
 
2.9%
8 497635
 
2.8%
7 444182
 
2.5%
Other values (23) 5815481
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18085344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2314035
 
12.8%
0 2281482
 
12.6%
1 2274112
 
12.6%
- 2260668
 
12.5%
u 591666
 
3.3%
J 552585
 
3.1%
a 537743
 
3.0%
e 515755
 
2.9%
8 497635
 
2.8%
7 444182
 
2.5%
Other values (23) 5815481
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18085344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2314035
 
12.8%
0 2281482
 
12.6%
1 2274112
 
12.6%
- 2260668
 
12.5%
u 591666
 
3.3%
J 552585
 
3.1%
a 537743
 
3.0%
e 515755
 
2.9%
8 497635
 
2.8%
7 444182
 
2.5%
Other values (23) 5815481
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18085344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2314035
 
12.8%
0 2281482
 
12.6%
1 2274112
 
12.6%
- 2260668
 
12.5%
u 591666
 
3.3%
J 552585
 
3.1%
a 537743
 
3.0%
e 515755
 
2.9%
8 497635
 
2.8%
7 444182
 
2.5%
Other values (23) 5815481
32.2%
Distinct9
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size125.3 MiB
2025-07-26T13:00:35.217222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length51
Median length50
Mean length9.110248829
Min length7

Characters and Unicode

Total characters20595248
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowFully Paid
3rd rowFully Paid
4th rowCurrent
5th rowFully Paid
ValueCountFrequency (%)
paid 1078739
29.2%
fully 1076751
29.2%
current 878317
23.8%
off 269320
 
7.3%
charged 268559
 
7.3%
late 25816
 
0.7%
days 25816
 
0.7%
31-120 21467
 
0.6%
grace 8436
 
0.2%
period 8436
 
0.2%
Other values (11) 32068
 
0.9%
2025-07-26T13:00:35.305617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 2160267
 
10.5%
r 2045575
 
9.9%
u 1959845
 
9.5%
1433057
 
7.0%
a 1410916
 
6.9%
d 1385060
 
6.7%
e 1204110
 
5.8%
C 1147637
 
5.6%
y 1107304
 
5.4%
i 1092673
 
5.3%
Other values (28) 5648804
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20595248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2160267
 
10.5%
r 2045575
 
9.9%
u 1959845
 
9.5%
1433057
 
7.0%
a 1410916
 
6.9%
d 1385060
 
6.7%
e 1204110
 
5.8%
C 1147637
 
5.6%
y 1107304
 
5.4%
i 1092673
 
5.3%
Other values (28) 5648804
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20595248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2160267
 
10.5%
r 2045575
 
9.9%
u 1959845
 
9.5%
1433057
 
7.0%
a 1410916
 
6.9%
d 1385060
 
6.7%
e 1204110
 
5.8%
C 1147637
 
5.6%
y 1107304
 
5.4%
i 1092673
 
5.3%
Other values (28) 5648804
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20595248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2160267
 
10.5%
r 2045575
 
9.9%
u 1959845
 
9.5%
1433057
 
7.0%
a 1410916
 
6.9%
d 1385060
 
6.7%
e 1204110
 
5.8%
C 1147637
 
5.6%
y 1107304
 
5.4%
i 1092673
 
5.3%
Other values (28) 5648804
27.4%
Distinct2
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size107.8 MiB
2025-07-26T13:00:35.333611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2260668
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rown
2nd rown
3rd rown
4th rown
5th rown
ValueCountFrequency (%)
n 2260048
> 99.9%
y 620
 
< 0.1%
2025-07-26T13:00:35.388057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2260048
> 99.9%
y 620
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2260048
> 99.9%
y 620
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2260048
> 99.9%
y 620
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2260048
> 99.9%
y 620
 
< 0.1%

url
URL

Distinct2260668
Distinct (%)100.0%
Missing33
Missing (%)< 0.1%
Memory size246.4 MiB
https://lendingclub.com/browse/loanDetail.action?loan_id=68407277
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=134288565
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=134545653
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=134958365
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=134108812
 
1
Other values (2260663)
2260663 
(Missing)
 
33
ValueCountFrequency (%)
https://lendingclub.com/browse/loanDetail.action?loan_id=68407277 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=134288565 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=134545653 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=134958365 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=134108812 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=134708872 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=134799148 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=134852755 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=134768457 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=134957574 1
 
< 0.1%
Other values (2260658) 2260658
> 99.9%
(Missing) 33
 
< 0.1%
ValueCountFrequency (%)
https 2260668
> 99.9%
(Missing) 33
 
< 0.1%
ValueCountFrequency (%)
lendingclub.com 2260668
> 99.9%
(Missing) 33
 
< 0.1%
ValueCountFrequency (%)
/browse/loanDetail.action 2260668
> 99.9%
(Missing) 33
 
< 0.1%
ValueCountFrequency (%)
loan_id=68407277 1
 
< 0.1%
loan_id=134288565 1
 
< 0.1%
loan_id=134898754 1
 
< 0.1%
loan_id=134545653 1
 
< 0.1%
loan_id=134958365 1
 
< 0.1%
loan_id=134108812 1
 
< 0.1%
loan_id=134708872 1
 
< 0.1%
loan_id=134799148 1
 
< 0.1%
loan_id=134852755 1
 
< 0.1%
loan_id=134923839 1
 
< 0.1%
Other values (2260658) 2260658
> 99.9%
(Missing) 33
 
< 0.1%
ValueCountFrequency (%)
2260668
> 99.9%
(Missing) 33
 
< 0.1%

desc
Text

Missing 

Distinct124500
Distinct (%)98.8%
Missing2134636
Missing (%)94.4%
Memory size100.3 MiB
2025-07-26T13:00:35.921866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3988
Median length2915
Mean length239.9687304
Min length1

Characters and Unicode

Total characters30251658
Distinct characters142
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123664 ?
Unique (%)98.1%

Sample

1st rowWe knew that using our credit cards to finance an adoption would squeeze us, but then medical and other unexpected expenses made the situation almost impossible. We are a stable family in a stable community. We just need to break a cycle of debt that is getting worse.
2nd rowI had a bad year two years ago, with some late and missed payments. I'm doing much better now, but I've got fees and some higher interest bits that have added up on top of the other stuff, and it's a little crazy. I'm hoping doing it thru Lending Club will make it easier - and cheaper - to pay off.
3rd rowLenders, I have the ability to pay off my current debt but, would like the ability to be able to put some extra money off to the side and build my personal savings account. The lower interest rate of roughly 7% would enable me to do that. I'm willing to share my credit report to anyone that is willing to help out. Please consider my application. Thank you,
4th row I paid off my first Prosper loan, but had an emergency and took out a second Prosper loan, but at a very high interest rate - and I would like the opportunity to get the interest rate lowered. This loan would be used to pay off the second Prosper Loan.
5th row
ValueCountFrequency (%)
to 207772
 
3.9%
on 187351
 
3.5%
i 182058
 
3.4%
156828
 
2.9%
added 146511
 
2.7%
borrower 146436
 
2.7%
and 138186
 
2.6%
my 132749
 
2.5%
a 124496
 
2.3%
the 114497
 
2.1%
Other values (85678) 3859588
71.5%
2025-07-26T13:00:36.195300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5610791
18.5%
e 2440743
 
8.1%
o 1999319
 
6.6%
a 1833374
 
6.1%
r 1711569
 
5.7%
t 1648389
 
5.4%
n 1558655
 
5.2%
d 1280178
 
4.2%
i 1246265
 
4.1%
s 1023905
 
3.4%
Other values (132) 9898470
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30251658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5610791
18.5%
e 2440743
 
8.1%
o 1999319
 
6.6%
a 1833374
 
6.1%
r 1711569
 
5.7%
t 1648389
 
5.4%
n 1558655
 
5.2%
d 1280178
 
4.2%
i 1246265
 
4.1%
s 1023905
 
3.4%
Other values (132) 9898470
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30251658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5610791
18.5%
e 2440743
 
8.1%
o 1999319
 
6.6%
a 1833374
 
6.1%
r 1711569
 
5.7%
t 1648389
 
5.4%
n 1558655
 
5.2%
d 1280178
 
4.2%
i 1246265
 
4.1%
s 1023905
 
3.4%
Other values (132) 9898470
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30251658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5610791
18.5%
e 2440743
 
8.1%
o 1999319
 
6.6%
a 1833374
 
6.1%
r 1711569
 
5.7%
t 1648389
 
5.4%
n 1558655
 
5.2%
d 1280178
 
4.2%
i 1246265
 
4.1%
s 1023905
 
3.4%
Other values (132) 9898470
32.7%
Distinct14
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size137.5 MiB
2025-07-26T13:00:36.254661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length18
Mean length14.79247594
Min length3

Characters and Unicode

Total characters33440877
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowsmall_business
3rd rowhome_improvement
4th rowdebt_consolidation
5th rowmajor_purchase
ValueCountFrequency (%)
debt_consolidation 1277877
56.5%
credit_card 516971
22.9%
home_improvement 150457
 
6.7%
other 139440
 
6.2%
major_purchase 50445
 
2.2%
medical 27488
 
1.2%
small_business 24689
 
1.1%
car 24013
 
1.1%
vacation 15525
 
0.7%
moving 15403
 
0.7%
Other values (4) 18360
 
0.8%
2025-07-26T13:00:36.348993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4369918
13.1%
d 3622318
10.8%
t 3378571
10.1%
i 3309066
9.9%
n 2767497
8.3%
e 2512421
7.5%
c 2429714
7.3%
_ 2021884
 
6.0%
a 2005271
 
6.0%
r 1451632
 
4.3%
Other values (12) 5572585
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33440877
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4369918
13.1%
d 3622318
10.8%
t 3378571
10.1%
i 3309066
9.9%
n 2767497
8.3%
e 2512421
7.5%
c 2429714
7.3%
_ 2021884
 
6.0%
a 2005271
 
6.0%
r 1451632
 
4.3%
Other values (12) 5572585
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33440877
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4369918
13.1%
d 3622318
10.8%
t 3378571
10.1%
i 3309066
9.9%
n 2767497
8.3%
e 2512421
7.5%
c 2429714
7.3%
_ 2021884
 
6.0%
a 2005271
 
6.0%
r 1451632
 
4.3%
Other values (12) 5572585
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33440877
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4369918
13.1%
d 3622318
10.8%
t 3378571
10.1%
i 3309066
9.9%
n 2767497
8.3%
e 2512421
7.5%
c 2429714
7.3%
_ 2021884
 
6.0%
a 2005271
 
6.0%
r 1451632
 
4.3%
Other values (12) 5572585
16.7%

title
Text

Missing 

Distinct63154
Distinct (%)2.8%
Missing23359
Missing (%)1.0%
Memory size143.0 MiB
2025-07-26T13:00:36.489102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length80
Median length18
Mean length17.69166314
Min length2

Characters and Unicode

Total characters39582301
Distinct characters108
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53972 ?
Unique (%)2.4%

Sample

1st rowDebt consolidation
2nd rowBusiness
3rd rowDebt consolidation
4th rowMajor purchase
5th rowDebt consolidation
ValueCountFrequency (%)
consolidation 1205787
24.7%
debt 1205393
24.7%
credit 500182
10.3%
card 492012
10.1%
refinancing 470712
 
9.7%
home 158543
 
3.3%
improvement 142244
 
2.9%
other 128158
 
2.6%
purchase 46224
 
0.9%
major 45589
 
0.9%
Other values (17775) 479382
 
9.8%
2025-07-26T13:00:36.719177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 4285974
10.8%
i 4217999
10.7%
o 4168357
10.5%
t 3282969
 
8.3%
e 3079270
 
7.8%
2645801
 
6.7%
a 2531407
 
6.4%
d 2302500
 
5.8%
c 2285045
 
5.8%
r 1933321
 
4.9%
Other values (98) 8849658
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39582301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 4285974
10.8%
i 4217999
10.7%
o 4168357
10.5%
t 3282969
 
8.3%
e 3079270
 
7.8%
2645801
 
6.7%
a 2531407
 
6.4%
d 2302500
 
5.8%
c 2285045
 
5.8%
r 1933321
 
4.9%
Other values (98) 8849658
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39582301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 4285974
10.8%
i 4217999
10.7%
o 4168357
10.5%
t 3282969
 
8.3%
e 3079270
 
7.8%
2645801
 
6.7%
a 2531407
 
6.4%
d 2302500
 
5.8%
c 2285045
 
5.8%
r 1933321
 
4.9%
Other values (98) 8849658
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39582301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 4285974
10.8%
i 4217999
10.7%
o 4168357
10.5%
t 3282969
 
8.3%
e 3079270
 
7.8%
2645801
 
6.7%
a 2531407
 
6.4%
d 2302500
 
5.8%
c 2285045
 
5.8%
r 1933321
 
4.9%
Other values (98) 8849658
22.4%
Distinct956
Distinct (%)< 0.1%
Missing34
Missing (%)< 0.1%
Memory size116.4 MiB
2025-07-26T13:00:36.860175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11303335
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)< 0.1%

Sample

1st row190xx
2nd row577xx
3rd row605xx
4th row076xx
5th row174xx
ValueCountFrequency (%)
112xx 23908
 
1.1%
945xx 23782
 
1.1%
750xx 23649
 
1.0%
606xx 21192
 
0.9%
300xx 20497
 
0.9%
331xx 19051
 
0.8%
070xx 18316
 
0.8%
770xx 17719
 
0.8%
891xx 17162
 
0.8%
100xx 17103
 
0.8%
Other values (946) 2058288
91.0%
2025-07-26T13:00:37.035000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
x 4521334
40.0%
0 1002006
 
8.9%
1 802911
 
7.1%
3 760539
 
6.7%
2 710832
 
6.3%
7 672375
 
5.9%
9 659951
 
5.8%
4 582456
 
5.2%
8 551306
 
4.9%
5 543273
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11303335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
x 4521334
40.0%
0 1002006
 
8.9%
1 802911
 
7.1%
3 760539
 
6.7%
2 710832
 
6.3%
7 672375
 
5.9%
9 659951
 
5.8%
4 582456
 
5.2%
8 551306
 
4.9%
5 543273
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11303335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
x 4521334
40.0%
0 1002006
 
8.9%
1 802911
 
7.1%
3 760539
 
6.7%
2 710832
 
6.3%
7 672375
 
5.9%
9 659951
 
5.8%
4 582456
 
5.2%
8 551306
 
4.9%
5 543273
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11303335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
x 4521334
40.0%
0 1002006
 
8.9%
1 802911
 
7.1%
3 760539
 
6.7%
2 710832
 
6.3%
7 672375
 
5.9%
9 659951
 
5.8%
4 582456
 
5.2%
8 551306
 
4.9%
5 543273
 
4.8%
Distinct51
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size110.0 MiB
2025-07-26T13:00:37.109898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4521336
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPA
2nd rowSD
3rd rowIL
4th rowNJ
5th rowPA
ValueCountFrequency (%)
ca 314533
 
13.9%
ny 186389
 
8.2%
tx 186335
 
8.2%
fl 161991
 
7.2%
il 91173
 
4.0%
nj 83132
 
3.7%
pa 76939
 
3.4%
oh 75132
 
3.3%
ga 74196
 
3.3%
va 62954
 
2.8%
Other values (41) 947894
41.9%
2025-07-26T13:00:37.205782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 756605
16.7%
N 511961
11.3%
C 494590
10.9%
L 306207
 
6.8%
T 283832
 
6.3%
M 276061
 
6.1%
I 242330
 
5.4%
Y 213024
 
4.7%
O 206879
 
4.6%
X 186335
 
4.1%
Other values (14) 1043512
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4521336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 756605
16.7%
N 511961
11.3%
C 494590
10.9%
L 306207
 
6.8%
T 283832
 
6.3%
M 276061
 
6.1%
I 242330
 
5.4%
Y 213024
 
4.7%
O 206879
 
4.6%
X 186335
 
4.1%
Other values (14) 1043512
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4521336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 756605
16.7%
N 511961
11.3%
C 494590
10.9%
L 306207
 
6.8%
T 283832
 
6.3%
M 276061
 
6.1%
I 242330
 
5.4%
Y 213024
 
4.7%
O 206879
 
4.6%
X 186335
 
4.1%
Other values (14) 1043512
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4521336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 756605
16.7%
N 511961
11.3%
C 494590
10.9%
L 306207
 
6.8%
T 283832
 
6.3%
M 276061
 
6.1%
I 242330
 
5.4%
Y 213024
 
4.7%
O 206879
 
4.6%
X 186335
 
4.1%
Other values (14) 1043512
23.1%

dti
Real number (ℝ)

Skewed 

Distinct10845
Distinct (%)0.5%
Missing1744
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean18.82419644
Minimum-1
Maximum999
Zeros1732
Zeros (%)0.1%
Negative2
Negative (%)< 0.1%
Memory size17.2 MiB
2025-07-26T13:00:37.259646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4.94
Q111.89
median17.84
Q324.49
95-th percentile33.88
Maximum999
Range1000
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation14.18332854
Coefficient of variation (CV)0.7534626294
Kurtosis1755.261278
Mean18.82419644
Median Absolute Deviation (MAD)6.27
Skewness29.20185447
Sum42523050.31
Variance201.1668086
MonotonicityNot monotonic
2025-07-26T13:00:37.308805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1732
 
0.1%
18 1584
 
0.1%
14.4 1577
 
0.1%
16.8 1576
 
0.1%
19.2 1566
 
0.1%
15.6 1506
 
0.1%
13.2 1496
 
0.1%
12 1486
 
0.1%
20.4 1424
 
0.1%
21.6 1391
 
0.1%
Other values (10835) 2243619
99.2%
(Missing) 1744
 
0.1%
ValueCountFrequency (%)
-1 2
 
< 0.1%
0 1732
0.1%
0.01 22
 
< 0.1%
0.02 35
 
< 0.1%
0.03 19
 
< 0.1%
ValueCountFrequency (%)
999 135
< 0.1%
995.6 1
 
< 0.1%
995.17 1
 
< 0.1%
994.4 1
 
< 0.1%
991.57 1
 
< 0.1%

delinq_2yrs
Real number (ℝ)

Zeros 

Distinct37
Distinct (%)< 0.1%
Missing62
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.3068791612
Minimum0
Maximum58
Zeros1839108
Zeros (%)81.4%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:37.351315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8672303329
Coefficient of variation (CV)2.825966839
Kurtosis73.35208962
Mean0.3068791612
Median Absolute Deviation (MAD)0
Skewness5.929811375
Sum693743
Variance0.7520884503
MonotonicityNot monotonic
2025-07-26T13:00:37.424109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 1839108
81.4%
1 281353
 
12.4%
2 81289
 
3.6%
3 29542
 
1.3%
4 13179
 
0.6%
5 6599
 
0.3%
6 3717
 
0.2%
7 2062
 
0.1%
8 1223
 
0.1%
9 818
 
< 0.1%
Other values (27) 1749
 
0.1%
ValueCountFrequency (%)
0 1839108
81.4%
1 281353
 
12.4%
2 81289
 
3.6%
3 29542
 
1.3%
4 13179
 
0.6%
ValueCountFrequency (%)
58 1
< 0.1%
42 1
< 0.1%
39 1
< 0.1%
36 1
< 0.1%
35 1
< 0.1%
Distinct754
Distinct (%)< 0.1%
Missing62
Missing (%)< 0.1%
Memory size122.9 MiB
2025-07-26T13:00:37.608669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters18085112
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)< 0.1%

Sample

1st rowAug-2003
2nd rowDec-1999
3rd rowAug-2000
4th rowSep-2008
5th rowJun-1998
ValueCountFrequency (%)
sep-2004 15400
 
0.7%
sep-2003 15215
 
0.7%
sep-2005 14780
 
0.7%
aug-2003 14669
 
0.6%
aug-2004 14413
 
0.6%
aug-2001 14355
 
0.6%
aug-2002 14322
 
0.6%
aug-2005 14207
 
0.6%
aug-2006 14143
 
0.6%
oct-2003 14108
 
0.6%
Other values (744) 2115027
93.6%
2025-07-26T13:00:37.799547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2665949
14.7%
- 2260639
 
12.5%
9 1872050
 
10.4%
2 1520438
 
8.4%
1 1333400
 
7.4%
u 586592
 
3.2%
e 563812
 
3.1%
a 524771
 
2.9%
J 522320
 
2.9%
8 406030
 
2.2%
Other values (23) 5829111
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18085112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2665949
14.7%
- 2260639
 
12.5%
9 1872050
 
10.4%
2 1520438
 
8.4%
1 1333400
 
7.4%
u 586592
 
3.2%
e 563812
 
3.1%
a 524771
 
2.9%
J 522320
 
2.9%
8 406030
 
2.2%
Other values (23) 5829111
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18085112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2665949
14.7%
- 2260639
 
12.5%
9 1872050
 
10.4%
2 1520438
 
8.4%
1 1333400
 
7.4%
u 586592
 
3.2%
e 563812
 
3.1%
a 524771
 
2.9%
J 522320
 
2.9%
8 406030
 
2.2%
Other values (23) 5829111
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18085112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2665949
14.7%
- 2260639
 
12.5%
9 1872050
 
10.4%
2 1520438
 
8.4%
1 1333400
 
7.4%
u 586592
 
3.2%
e 563812
 
3.1%
a 524771
 
2.9%
J 522320
 
2.9%
8 406030
 
2.2%
Other values (23) 5829111
32.2%

fico_range_low
Real number (ℝ)

Distinct48
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean698.5882049
Minimum610
Maximum845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:37.860242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum610
5-th percentile660
Q1675
median690
Q3715
95-th percentile765
Maximum845
Range235
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.01037645
Coefficient of variation (CV)0.04725298283
Kurtosis1.315168369
Mean698.5882049
Median Absolute Deviation (MAD)20
Skewness1.192877206
Sum1579276000
Variance1089.684953
MonotonicityNot monotonic
2025-07-26T13:00:37.909164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
660 186580
 
8.3%
670 182119
 
8.1%
665 180759
 
8.0%
680 167199
 
7.4%
675 164016
 
7.3%
685 148009
 
6.5%
690 144690
 
6.4%
695 130941
 
5.8%
700 124184
 
5.5%
705 113074
 
5.0%
Other values (38) 719097
31.8%
ValueCountFrequency (%)
610 2
 
< 0.1%
615 1
 
< 0.1%
620 1
 
< 0.1%
625 2
 
< 0.1%
630 6
< 0.1%
ValueCountFrequency (%)
845 441
 
< 0.1%
840 572
 
< 0.1%
835 859
 
< 0.1%
830 1439
0.1%
825 2189
0.1%

fico_range_high
Real number (ℝ)

Distinct48
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean702.5884
Minimum614
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:37.953648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum614
5-th percentile664
Q1679
median694
Q3719
95-th percentile769
Maximum850
Range236
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.01124462
Coefficient of variation (CV)0.0469851831
Kurtosis1.316769731
Mean702.5884
Median Absolute Deviation (MAD)20
Skewness1.193116484
Sum1588319113
Variance1089.742271
MonotonicityNot monotonic
2025-07-26T13:00:37.997388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
664 186580
 
8.3%
674 182119
 
8.1%
669 180759
 
8.0%
684 167199
 
7.4%
679 164016
 
7.3%
689 148009
 
6.5%
694 144690
 
6.4%
699 130941
 
5.8%
704 124184
 
5.5%
709 113074
 
5.0%
Other values (38) 719097
31.8%
ValueCountFrequency (%)
614 2
 
< 0.1%
619 1
 
< 0.1%
624 1
 
< 0.1%
629 2
 
< 0.1%
634 6
< 0.1%
ValueCountFrequency (%)
850 441
 
< 0.1%
844 572
 
< 0.1%
839 859
 
< 0.1%
834 1439
0.1%
829 2189
0.1%

inq_last_6mths
Real number (ℝ)

Zeros 

Distinct28
Distinct (%)< 0.1%
Missing63
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.5768353889
Minimum0
Maximum33
Zeros1381722
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:38.046954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8859631584
Coefficient of variation (CV)1.53590292
Kurtosis9.57608952
Mean0.5768353889
Median Absolute Deviation (MAD)0
Skewness2.066186683
Sum1304016
Variance0.784930718
MonotonicityNot monotonic
2025-07-26T13:00:38.092480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 1381722
61.1%
1 584390
25.8%
2 200212
 
8.9%
3 69009
 
3.1%
4 17380
 
0.8%
5 6232
 
0.3%
6 1231
 
0.1%
7 195
 
< 0.1%
8 122
 
< 0.1%
9 50
 
< 0.1%
Other values (18) 95
 
< 0.1%
(Missing) 63
 
< 0.1%
ValueCountFrequency (%)
0 1381722
61.1%
1 584390
25.8%
2 200212
 
8.9%
3 69009
 
3.1%
4 17380
 
0.8%
ValueCountFrequency (%)
33 1
< 0.1%
32 1
< 0.1%
31 1
< 0.1%
28 1
< 0.1%
27 1
< 0.1%

mths_since_last_delinq
Real number (ℝ)

Missing 

Distinct173
Distinct (%)< 0.1%
Missing1158535
Missing (%)51.2%
Infinite0
Infinite (%)0.0%
Mean34.5409158
Minimum0
Maximum226
Zeros2637
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:38.131580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q116
median31
Q350
95-th percentile74
Maximum226
Range226
Interquartile range (IQR)34

Descriptive statistics

Standard deviation21.9004709
Coefficient of variation (CV)0.6340443035
Kurtosis-0.6936426553
Mean34.5409158
Median Absolute Deviation (MAD)17
Skewness0.4597452469
Sum38069823
Variance479.6306255
MonotonicityNot monotonic
2025-07-26T13:00:38.174918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 20967
 
0.9%
13 20629
 
0.9%
6 20615
 
0.9%
9 20345
 
0.9%
7 20139
 
0.9%
14 19913
 
0.9%
8 19575
 
0.9%
15 19555
 
0.9%
10 19371
 
0.9%
18 19042
 
0.8%
Other values (163) 902015
39.9%
(Missing) 1158535
51.2%
ValueCountFrequency (%)
0 2637
 
0.1%
1 7100
0.3%
2 9853
0.4%
3 13089
0.6%
4 15506
0.7%
ValueCountFrequency (%)
226 1
< 0.1%
202 1
< 0.1%
195 1
< 0.1%
192 1
< 0.1%
188 2
< 0.1%

mths_since_last_record
Real number (ℝ)

Missing 

Distinct129
Distinct (%)< 0.1%
Missing1901545
Missing (%)84.1%
Infinite0
Infinite (%)0.0%
Mean72.31284177
Minimum0
Maximum129
Zeros1296
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:38.234464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q155
median74
Q392
95-th percentile113
Maximum129
Range129
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.46409448
Coefficient of variation (CV)0.3659667333
Kurtosis-0.4147973573
Mean72.31284177
Median Absolute Deviation (MAD)19
Skewness-0.3692538065
Sum25971591
Variance700.3482964
MonotonicityNot monotonic
2025-07-26T13:00:38.310479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 5448
 
0.2%
80 5434
 
0.2%
82 5405
 
0.2%
77 5356
 
0.2%
76 5326
 
0.2%
81 5278
 
0.2%
78 5263
 
0.2%
75 5262
 
0.2%
72 5176
 
0.2%
71 5164
 
0.2%
Other values (119) 306044
 
13.5%
(Missing) 1901545
84.1%
ValueCountFrequency (%)
0 1296
0.1%
1 162
 
< 0.1%
2 180
 
< 0.1%
3 316
 
< 0.1%
4 385
 
< 0.1%
ValueCountFrequency (%)
129 1
 
< 0.1%
127 1
 
< 0.1%
126 4
 
< 0.1%
125 3
 
< 0.1%
124 13
< 0.1%

open_acc
Real number (ℝ)

Distinct91
Distinct (%)< 0.1%
Missing62
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11.61240207
Minimum0
Maximum101
Zeros56
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:38.367441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q314
95-th percentile22
Maximum101
Range101
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.640861338
Coefficient of variation (CV)0.4857618006
Kurtosis3.446376782
Mean11.61240207
Median Absolute Deviation (MAD)3
Skewness1.315544951
Sum26251449
Variance31.81931664
MonotonicityNot monotonic
2025-07-26T13:00:38.422256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 195762
 
8.7%
10 189737
 
8.4%
8 188717
 
8.3%
11 175101
 
7.7%
7 172834
 
7.6%
12 157331
 
7.0%
6 145444
 
6.4%
13 137502
 
6.1%
14 118314
 
5.2%
5 108565
 
4.8%
Other values (81) 671332
29.7%
ValueCountFrequency (%)
0 56
 
< 0.1%
1 1644
 
0.1%
2 10860
 
0.5%
3 32428
1.4%
4 67827
3.0%
ValueCountFrequency (%)
101 1
< 0.1%
97 1
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
91 1
< 0.1%

pub_rec
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)< 0.1%
Missing62
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.1975277787
Minimum0
Maximum86
Zeros1902758
Zeros (%)84.2%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:38.477166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum86
Range86
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5705150143
Coefficient of variation (CV)2.888277377
Kurtosis704.1159105
Mean0.1975277787
Median Absolute Deviation (MAD)0
Skewness11.37680843
Sum446539
Variance0.3254873815
MonotonicityNot monotonic
2025-07-26T13:00:38.559788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 1902758
84.2%
1 305390
 
13.5%
2 34154
 
1.5%
3 10567
 
0.5%
4 3872
 
0.2%
5 1843
 
0.1%
6 933
 
< 0.1%
7 427
 
< 0.1%
8 243
 
< 0.1%
9 143
 
< 0.1%
Other values (33) 309
 
< 0.1%
ValueCountFrequency (%)
0 1902758
84.2%
1 305390
 
13.5%
2 34154
 
1.5%
3 10567
 
0.5%
4 3872
 
0.2%
ValueCountFrequency (%)
86 1
< 0.1%
63 1
< 0.1%
61 2
< 0.1%
54 1
< 0.1%
52 1
< 0.1%

revol_bal
Real number (ℝ)

Distinct102251
Distinct (%)4.5%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16658.45808
Minimum0
Maximum2904836
Zeros12562
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:38.686695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1572
Q15950
median11324
Q320246
95-th percentile45164
Maximum2904836
Range2904836
Interquartile range (IQR)14296

Descriptive statistics

Standard deviation22948.30503
Coefficient of variation (CV)1.377576779
Kurtosis643.1980554
Mean16658.45808
Median Absolute Deviation (MAD)6381
Skewness13.23198843
Sum3.765924311 × 1010
Variance526624703.6
MonotonicityNot monotonic
2025-07-26T13:00:38.783855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12562
 
0.6%
8 216
 
< 0.1%
10 170
 
< 0.1%
2 169
 
< 0.1%
5 160
 
< 0.1%
5235 160
 
< 0.1%
6312 158
 
< 0.1%
5849 158
 
< 0.1%
5265 156
 
< 0.1%
6118 156
 
< 0.1%
Other values (102241) 2246603
99.4%
ValueCountFrequency (%)
0 12562
0.6%
1 123
 
< 0.1%
2 169
 
< 0.1%
3 151
 
< 0.1%
4 153
 
< 0.1%
ValueCountFrequency (%)
2904836 1
< 0.1%
2568995 1
< 0.1%
2560703 1
< 0.1%
2559552 1
< 0.1%
2358150 1
< 0.1%

revol_util
Real number (ℝ)

Distinct1430
Distinct (%)0.1%
Missing1835
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean50.33769625
Minimum0
Maximum892.3
Zeros13069
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:38.854457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.4
Q131.5
median50.3
Q369.4
95-th percentile91
Maximum892.3
Range892.3
Interquartile range (IQR)37.9

Descriptive statistics

Standard deviation24.71307332
Coefficient of variation (CV)0.4909456563
Kurtosis-0.2226717499
Mean50.33769625
Median Absolute Deviation (MAD)18.9
Skewness0.01255594308
Sum113706110.6
Variance610.735993
MonotonicityNot monotonic
2025-07-26T13:00:38.917479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13069
 
0.6%
57 4324
 
0.2%
48 4283
 
0.2%
59 4272
 
0.2%
61 4223
 
0.2%
54 4190
 
0.2%
58 4188
 
0.2%
53 4185
 
0.2%
55 4181
 
0.2%
51 4175
 
0.2%
Other values (1420) 2207776
97.7%
ValueCountFrequency (%)
0 13069
0.6%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
ValueCountFrequency (%)
892.3 1
< 0.1%
366.6 1
< 0.1%
193 1
< 0.1%
191 1
< 0.1%
184.6 1
< 0.1%

total_acc
Real number (ℝ)

Distinct152
Distinct (%)< 0.1%
Missing62
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean24.16255227
Minimum1
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:38.987163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q115
median22
Q331
95-th percentile46
Maximum176
Range175
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.98752832
Coefficient of variation (CV)0.4961201194
Kurtosis1.848815197
Mean24.16255227
Median Absolute Deviation (MAD)8
Skewness1.007455501
Sum54622808
Variance143.7008352
MonotonicityNot monotonic
2025-07-26T13:00:39.101972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 82570
 
3.7%
19 82012
 
3.6%
18 81931
 
3.6%
17 81378
 
3.6%
21 81170
 
3.6%
16 79655
 
3.5%
22 79438
 
3.5%
23 77691
 
3.4%
15 77146
 
3.4%
24 75330
 
3.3%
Other values (142) 1462318
64.7%
ValueCountFrequency (%)
1 21
 
< 0.1%
2 1333
 
0.1%
3 4244
 
0.2%
4 10456
0.5%
5 16398
0.7%
ValueCountFrequency (%)
176 1
< 0.1%
173 1
< 0.1%
169 1
< 0.1%
165 1
< 0.1%
162 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size107.8 MiB
2025-07-26T13:00:39.141840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2260668
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roww
2nd roww
3rd roww
4th roww
5th roww
ValueCountFrequency (%)
w 1535467
67.9%
f 725201
32.1%
2025-07-26T13:00:39.215927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 1535467
67.9%
f 725201
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 1535467
67.9%
f 725201
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 1535467
67.9%
f 725201
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 1535467
67.9%
f 725201
32.1%

out_prncp
Real number (ℝ)

Zeros 

Distinct356141
Distinct (%)15.8%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4206.891439
Minimum0
Maximum40000
Zeros1352764
Zeros (%)59.8%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:39.271644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36149.94
95-th percentile21077.135
Maximum40000
Range40000
Interquartile range (IQR)6149.94

Descriptive statistics

Standard deviation7343.238522
Coefficient of variation (CV)1.745526032
Kurtosis4.027761014
Mean4206.891439
Median Absolute Deviation (MAD)0
Skewness2.072895804
Sum9510384856
Variance53923151.99
MonotonicityNot monotonic
2025-07-26T13:00:39.351149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1352764
59.8%
8717.99 323
 
< 0.1%
8457.66 311
 
< 0.1%
8757.61 258
 
< 0.1%
33830.65 249
 
< 0.1%
8196.01 244
 
< 0.1%
9238.49 242
 
< 0.1%
8747.25 241
 
< 0.1%
7688.11 234
 
< 0.1%
8503.85 228
 
< 0.1%
Other values (356131) 905574
40.1%
ValueCountFrequency (%)
0 1352764
59.8%
0.01 6
 
< 0.1%
0.02 4
 
< 0.1%
0.03 2
 
< 0.1%
0.04 7
 
< 0.1%
ValueCountFrequency (%)
40000 3
< 0.1%
39595.02 1
 
< 0.1%
39541.82 1
 
< 0.1%
39507.58 1
 
< 0.1%
39501.1 1
 
< 0.1%

out_prncp_inv
Real number (ℝ)

Zeros 

Distinct368481
Distinct (%)16.3%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4205.965357
Minimum0
Maximum40000
Zeros1352764
Zeros (%)59.8%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:39.441777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36146.31
95-th percentile21076
Maximum40000
Range40000
Interquartile range (IQR)6146.31

Descriptive statistics

Standard deviation7342.332972
Coefficient of variation (CV)1.745695066
Kurtosis4.02985103
Mean4205.965357
Median Absolute Deviation (MAD)0
Skewness2.073302878
Sum9508291291
Variance53909853.48
MonotonicityNot monotonic
2025-07-26T13:00:39.522427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1352764
59.8%
8717.99 316
 
< 0.1%
8457.66 310
 
< 0.1%
8757.61 254
 
< 0.1%
8196.01 242
 
< 0.1%
9238.49 240
 
< 0.1%
33830.65 237
 
< 0.1%
8747.25 237
 
< 0.1%
7688.11 233
 
< 0.1%
8503.85 227
 
< 0.1%
Other values (368471) 905608
40.1%
ValueCountFrequency (%)
0 1352764
59.8%
0.01 6
 
< 0.1%
0.02 4
 
< 0.1%
0.03 2
 
< 0.1%
0.04 7
 
< 0.1%
ValueCountFrequency (%)
40000 3
< 0.1%
39595.02 1
 
< 0.1%
39541.82 1
 
< 0.1%
39507.58 1
 
< 0.1%
39501.1 1
 
< 0.1%

total_pymnt
Real number (ℝ)

Distinct1633866
Distinct (%)72.3%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12082.55683
Minimum0
Maximum63296.87792
Zeros949
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:39.640012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1362.37
Q14546.4575
median9329.72
Q316940.86937
95-th percentile32824.63251
Maximum63296.87792
Range63296.87792
Interquartile range (IQR)12394.41187

Descriptive statistics

Standard deviation9901.383185
Coefficient of variation (CV)0.8194774769
Kurtosis1.354744602
Mean12082.55683
Median Absolute Deviation (MAD)5633.861795
Skewness1.269316227
Sum2.731464958 × 1010
Variance98037388.98
MonotonicityNot monotonic
2025-07-26T13:00:39.718902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 949
 
< 0.1%
10838.35484 202
 
< 0.1%
11258.43637 190
 
< 0.1%
16257.52756 180
 
< 0.1%
2129.65 177
 
< 0.1%
1520.21 176
 
< 0.1%
13510.12859 166
 
< 0.1%
1572.8 154
 
< 0.1%
13006.0278 150
 
< 0.1%
2164.17 147
 
< 0.1%
Other values (1633856) 2258177
99.9%
ValueCountFrequency (%)
0 949
< 0.1%
0.75 1
 
< 0.1%
0.8 1
 
< 0.1%
10 1
 
< 0.1%
16.58 1
 
< 0.1%
ValueCountFrequency (%)
63296.87792 1
< 0.1%
62948.99096 1
< 0.1%
62884.79738 1
< 0.1%
62862.50673 1
< 0.1%
62837.63969 1
< 0.1%

total_pymnt_inv
Real number (ℝ)

Distinct1311099
Distinct (%)58.0%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12064.39465
Minimum0
Maximum63296.88
Zeros1227
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:39.818002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1352.57
Q14531.8
median9309.675
Q316916.705
95-th percentile32803.403
Maximum63296.88
Range63296.88
Interquartile range (IQR)12384.905

Descriptive statistics

Standard deviation9896.991745
Coefficient of variation (CV)0.8203471478
Kurtosis1.356326811
Mean12064.39465
Median Absolute Deviation (MAD)5625.9
Skewness1.270054921
Sum2.727359093 × 1010
Variance97950445.6
MonotonicityNot monotonic
2025-07-26T13:00:39.878201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1227
 
0.1%
11431.12 266
 
< 0.1%
11784.23 265
 
< 0.1%
11258.44 262
 
< 0.1%
10838.35 248
 
< 0.1%
11471.31 218
 
< 0.1%
13510.13 215
 
< 0.1%
10956.78 213
 
< 0.1%
16257.53 211
 
< 0.1%
12128.02 211
 
< 0.1%
Other values (1311089) 2257332
99.9%
ValueCountFrequency (%)
0 1227
0.1%
0.51 1
 
< 0.1%
0.54 1
 
< 0.1%
0.75 1
 
< 0.1%
0.8 1
 
< 0.1%
ValueCountFrequency (%)
63296.88 1
< 0.1%
62904.03 1
< 0.1%
62862.51 1
< 0.1%
62839.88 1
< 0.1%
62837.64 1
< 0.1%

total_rec_prncp
Real number (ℝ)

Distinct486463
Distinct (%)21.5%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9505.771588
Minimum0
Maximum40000
Zeros2576
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:39.964628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile829.55
Q13000
median7000
Q313899.1
95-th percentile27575
Maximum40000
Range40000
Interquartile range (IQR)10899.1

Descriptive statistics

Standard deviation8321.852079
Coefficient of variation (CV)0.8754525608
Kurtosis1.117038198
Mean9505.771588
Median Absolute Deviation (MAD)4701.3
Skewness1.256020411
Sum2.148939364 × 1010
Variance69253222.03
MonotonicityNot monotonic
2025-07-26T13:00:40.052806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 81386
 
3.6%
12000 59540
 
2.6%
15000 56526
 
2.5%
20000 55609
 
2.5%
5000 41118
 
1.8%
8000 39897
 
1.8%
35000 38665
 
1.7%
6000 38419
 
1.7%
16000 28831
 
1.3%
25000 27008
 
1.2%
Other values (486453) 1793669
79.3%
ValueCountFrequency (%)
0 2576
0.1%
0.01 1
 
< 0.1%
2.13 1
 
< 0.1%
5.03 1
 
< 0.1%
5.62 1
 
< 0.1%
ValueCountFrequency (%)
40000 5396
0.2%
39999.53 1
 
< 0.1%
39999.42 1
 
< 0.1%
39995.76 1
 
< 0.1%
39989.79 1
 
< 0.1%

total_rec_int
Real number (ℝ)

Distinct635921
Distinct (%)28.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2431.387654
Minimum0
Maximum28192.5
Zeros2656
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:40.152110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201.82
Q1728.1875
median1525.94
Q33108.0625
95-th percentile7821.4065
Maximum28192.5
Range28192.5
Interquartile range (IQR)2379.875

Descriptive statistics

Standard deviation2679.73784
Coefficient of variation (CV)1.102143393
Kurtosis9.162197563
Mean2431.387654
Median Absolute Deviation (MAD)978.94
Skewness2.552499573
Sum5496560266
Variance7180994.889
MonotonicityNot monotonic
2025-07-26T13:00:40.235689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2656
 
0.1%
1431.12 315
 
< 0.1%
1784.23 309
 
< 0.1%
1258.44 272
 
< 0.1%
838.35 251
 
< 0.1%
1955.4 245
 
< 0.1%
956.78 245
 
< 0.1%
1471.31 240
 
< 0.1%
1977.77 230
 
< 0.1%
2128.02 228
 
< 0.1%
Other values (635911) 2255677
99.8%
ValueCountFrequency (%)
0 2656
0.1%
0.01 28
 
< 0.1%
0.06 1
 
< 0.1%
0.07 2
 
< 0.1%
0.12 1
 
< 0.1%
ValueCountFrequency (%)
28192.5 1
< 0.1%
27948.99 1
< 0.1%
27922.66 1
< 0.1%
27884.8 1
< 0.1%
27876.77 1
< 0.1%

total_rec_late_fee
Real number (ℝ)

Skewed  Zeros 

Distinct18375
Distinct (%)0.8%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.518453038
Minimum-9.5 × 10-9
Maximum1484.34
Zeros2173513
Zeros (%)96.1%
Negative8
Negative (%)< 0.1%
Memory size17.2 MiB
2025-07-26T13:00:40.335921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-9.5 × 10-9
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1484.34
Range1484.34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.84159167
Coefficient of variation (CV)7.798457627
Kurtosis957.5469612
Mean1.518453038
Median Absolute Deviation (MAD)0
Skewness21.90352305
Sum3432718.192
Variance140.2232934
MonotonicityNot monotonic
2025-07-26T13:00:40.431915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2173513
96.1%
15 18145
 
0.8%
30 3738
 
0.2%
45 1296
 
0.1%
60 536
 
< 0.1%
75 253
 
< 0.1%
90 137
 
< 0.1%
16.61 133
 
< 0.1%
16.37 118
 
< 0.1%
15.94 111
 
< 0.1%
Other values (18365) 62688
 
2.8%
ValueCountFrequency (%)
-9.5 × 10-91
< 0.1%
-5.1 × 10-91
< 0.1%
-3.9 × 10-91
< 0.1%
-2 × 10-91
< 0.1%
-1.8 × 10-91
< 0.1%
ValueCountFrequency (%)
1484.34 1
< 0.1%
1188.83 1
< 0.1%
1098.360001 1
< 0.1%
1007.36 1
< 0.1%
983.43 1
< 0.1%

recoveries
Real number (ℝ)

Zeros 

Distinct132777
Distinct (%)5.9%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean143.8791352
Minimum0
Maximum39859.55
Zeros2075236
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:40.522429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile892.543
Maximum39859.55
Range39859.55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation748.1640051
Coefficient of variation (CV)5.199947887
Kurtosis196.8034342
Mean143.8791352
Median Absolute Deviation (MAD)0
Skewness10.60601568
Sum325262956.9
Variance559749.3785
MonotonicityNot monotonic
2025-07-26T13:00:40.621572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2075236
91.8%
50 779
 
< 0.1%
100 638
 
< 0.1%
150 594
 
< 0.1%
200 563
 
< 0.1%
300 415
 
< 0.1%
75 322
 
< 0.1%
250 289
 
< 0.1%
25 246
 
< 0.1%
125 236
 
< 0.1%
Other values (132767) 181350
 
8.0%
ValueCountFrequency (%)
0 2075236
91.8%
0.01 15
 
< 0.1%
0.02 16
 
< 0.1%
0.03 25
 
< 0.1%
0.04 39
 
< 0.1%
ValueCountFrequency (%)
39859.55 1
< 0.1%
39444.37 1
< 0.1%
37153.46 1
< 0.1%
36578.54 1
< 0.1%
35581.88 1
< 0.1%

collection_recovery_fee
Real number (ℝ)

Zeros 

Distinct146222
Distinct (%)6.5%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.98256586
Minimum0
Maximum7174.719
Zeros2083655
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:40.700089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile134.153685
Maximum7174.719
Range7174.719
Interquartile range (IQR)0

Descriptive statistics

Standard deviation131.225587
Coefficient of variation (CV)5.471707562
Kurtosis224.48381
Mean23.98256586
Median Absolute Deviation (MAD)0
Skewness11.34307972
Sum54216619.2
Variance17220.15467
MonotonicityNot monotonic
2025-07-26T13:00:40.787114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2083655
92.2%
9 637
 
< 0.1%
18 486
 
< 0.1%
27 455
 
< 0.1%
36 428
 
< 0.1%
54 318
 
< 0.1%
13.5 236
 
< 0.1%
45 234
 
< 0.1%
4.5 174
 
< 0.1%
22.5 171
 
< 0.1%
Other values (146212) 173874
 
7.7%
ValueCountFrequency (%)
0 2083655
92.2%
0.0018 1
 
< 0.1%
0.018 1
 
< 0.1%
0.036 1
 
< 0.1%
0.0378 1
 
< 0.1%
ValueCountFrequency (%)
7174.719 1
< 0.1%
7002.19 1
< 0.1%
6972.59 1
< 0.1%
6687.6228 1
< 0.1%
6584.1372 1
< 0.1%
Distinct136
Distinct (%)< 0.1%
Missing2460
Missing (%)0.1%
Memory size122.8 MiB
2025-07-26T13:00:40.932255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters18065928
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan-2019
2nd rowJun-2016
3rd rowJun-2017
4th rowFeb-2019
5th rowJul-2016
ValueCountFrequency (%)
mar-2019 853003
37.8%
feb-2019 97074
 
4.3%
aug-2018 39615
 
1.8%
jan-2019 38483
 
1.7%
mar-2018 38269
 
1.7%
oct-2018 37133
 
1.6%
jul-2018 36498
 
1.6%
nov-2018 35296
 
1.6%
jun-2018 35168
 
1.6%
may-2018 33456
 
1.5%
Other values (126) 1014246
44.9%
2025-07-26T13:00:41.116600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2270054
12.6%
1 2262814
12.5%
0 2261836
12.5%
- 2258241
12.5%
a 1190481
 
6.6%
M 1063591
 
5.9%
r 1057928
 
5.9%
9 989398
 
5.5%
8 416227
 
2.3%
e 401870
 
2.2%
Other values (23) 3893488
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18065928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2270054
12.6%
1 2262814
12.5%
0 2261836
12.5%
- 2258241
12.5%
a 1190481
 
6.6%
M 1063591
 
5.9%
r 1057928
 
5.9%
9 989398
 
5.5%
8 416227
 
2.3%
e 401870
 
2.2%
Other values (23) 3893488
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18065928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2270054
12.6%
1 2262814
12.5%
0 2261836
12.5%
- 2258241
12.5%
a 1190481
 
6.6%
M 1063591
 
5.9%
r 1057928
 
5.9%
9 989398
 
5.5%
8 416227
 
2.3%
e 401870
 
2.2%
Other values (23) 3893488
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18065928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2270054
12.6%
1 2262814
12.5%
0 2261836
12.5%
- 2258241
12.5%
a 1190481
 
6.6%
M 1063591
 
5.9%
r 1057928
 
5.9%
9 989398
 
5.5%
8 416227
 
2.3%
e 401870
 
2.2%
Other values (23) 3893488
21.6%

last_pymnt_amnt
Real number (ℝ)

Distinct704467
Distinct (%)31.2%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3429.345942
Minimum0
Maximum42192.05
Zeros2893
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:41.188537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100.54
Q1310.33
median600.57
Q33743.75
95-th percentile16926.714
Maximum42192.05
Range42192.05
Interquartile range (IQR)3433.42

Descriptive statistics

Standard deviation6018.247582
Coefficient of variation (CV)1.754925774
Kurtosis7.082541403
Mean3429.345942
Median Absolute Deviation (MAD)396.69
Skewness2.567204711
Sum7752612632
Variance36219303.96
MonotonicityNot monotonic
2025-07-26T13:00:41.240763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 3414
 
0.2%
0 2893
 
0.1%
100 2082
 
0.1%
332.1 1809
 
0.1%
301.15 1790
 
0.1%
361.38 1709
 
0.1%
309.74 1524
 
0.1%
320.05 1453
 
0.1%
324.65 1451
 
0.1%
304.72 1387
 
0.1%
Other values (704457) 2241156
99.1%
ValueCountFrequency (%)
0 2893
0.1%
0.01 296
 
< 0.1%
0.02 97
 
< 0.1%
0.03 83
 
< 0.1%
0.04 79
 
< 0.1%
ValueCountFrequency (%)
42192.05 1
< 0.1%
42148.53 1
< 0.1%
42005.2 1
< 0.1%
41453.07 1
< 0.1%
41434 1
< 0.1%

next_pymnt_d
Text

Missing 

Distinct106
Distinct (%)< 0.1%
Missing1345343
Missing (%)59.5%
Memory size90.8 MiB
2025-07-26T13:00:41.334567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters7322864
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowApr-2019
2nd rowApr-2019
3rd rowApr-2019
4th rowApr-2019
5th rowApr-2019
ValueCountFrequency (%)
apr-2019 912221
99.7%
mar-2019 277
 
< 0.1%
mar-2011 107
 
< 0.1%
may-2019 105
 
< 0.1%
apr-2011 101
 
< 0.1%
feb-2011 91
 
< 0.1%
jan-2011 79
 
< 0.1%
may-2011 77
 
< 0.1%
dec-2010 71
 
< 0.1%
jun-2011 66
 
< 0.1%
Other values (96) 2163
 
0.2%
2025-07-26T13:00:41.468346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 916291
12.5%
2 915830
12.5%
1 915760
12.5%
- 915358
12.5%
r 913013
12.5%
9 912916
12.5%
A 912687
12.5%
p 912676
12.5%
a 1103
 
< 0.1%
M 883
 
< 0.1%
Other values (22) 6347
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7322864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 916291
12.5%
2 915830
12.5%
1 915760
12.5%
- 915358
12.5%
r 913013
12.5%
9 912916
12.5%
A 912687
12.5%
p 912676
12.5%
a 1103
 
< 0.1%
M 883
 
< 0.1%
Other values (22) 6347
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7322864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 916291
12.5%
2 915830
12.5%
1 915760
12.5%
- 915358
12.5%
r 913013
12.5%
9 912916
12.5%
A 912687
12.5%
p 912676
12.5%
a 1103
 
< 0.1%
M 883
 
< 0.1%
Other values (22) 6347
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7322864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 916291
12.5%
2 915830
12.5%
1 915760
12.5%
- 915358
12.5%
r 913013
12.5%
9 912916
12.5%
A 912687
12.5%
p 912676
12.5%
a 1103
 
< 0.1%
M 883
 
< 0.1%
Other values (22) 6347
 
0.1%
Distinct141
Distinct (%)< 0.1%
Missing105
Missing (%)< 0.1%
Memory size122.9 MiB
2025-07-26T13:00:41.563354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters18084768
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowMar-2019
2nd rowMar-2019
3rd rowMar-2019
4th rowMar-2019
5th rowMar-2018
ValueCountFrequency (%)
mar-2019 1371381
60.7%
feb-2019 76956
 
3.4%
jan-2019 63283
 
2.8%
jul-2018 54113
 
2.4%
oct-2016 50983
 
2.3%
oct-2018 47357
 
2.1%
dec-2018 45199
 
2.0%
aug-2018 43656
 
1.9%
nov-2018 43632
 
1.9%
sep-2018 33614
 
1.5%
Other values (131) 430422
 
19.0%
2025-07-26T13:00:41.693930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2263789
12.5%
1 2261988
12.5%
0 2261683
12.5%
- 2260596
12.5%
a 1563459
8.6%
9 1511856
8.4%
r 1463253
8.1%
M 1461026
8.1%
8 393544
 
2.2%
e 268453
 
1.5%
Other values (23) 2375121
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18084768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2263789
12.5%
1 2261988
12.5%
0 2261683
12.5%
- 2260596
12.5%
a 1563459
8.6%
9 1511856
8.4%
r 1463253
8.1%
M 1461026
8.1%
8 393544
 
2.2%
e 268453
 
1.5%
Other values (23) 2375121
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18084768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2263789
12.5%
1 2261988
12.5%
0 2261683
12.5%
- 2260596
12.5%
a 1563459
8.6%
9 1511856
8.4%
r 1463253
8.1%
M 1461026
8.1%
8 393544
 
2.2%
e 268453
 
1.5%
Other values (23) 2375121
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18084768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2263789
12.5%
1 2261988
12.5%
0 2261683
12.5%
- 2260596
12.5%
a 1563459
8.6%
9 1511856
8.4%
r 1463253
8.1%
M 1461026
8.1%
8 393544
 
2.2%
e 268453
 
1.5%
Other values (23) 2375121
13.1%

last_fico_range_high
Real number (ℝ)

Distinct72
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean687.6609953
Minimum0
Maximum850
Zeros235
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:41.747209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile539
Q1654
median699
Q3734
95-th percentile794
Maximum850
Range850
Interquartile range (IQR)80

Descriptive statistics

Standard deviation72.97043535
Coefficient of variation (CV)0.1061139658
Kurtosis0.9163633575
Mean687.6609953
Median Absolute Deviation (MAD)40
Skewness-0.7567365639
Sum1554573207
Variance5324.684435
MonotonicityNot monotonic
2025-07-26T13:00:41.795656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
694 83697
 
3.7%
699 82915
 
3.7%
709 82358
 
3.6%
704 82319
 
3.6%
714 79410
 
3.5%
684 77821
 
3.4%
719 77448
 
3.4%
689 76599
 
3.4%
724 75229
 
3.3%
679 68535
 
3.0%
Other values (62) 1474337
65.2%
ValueCountFrequency (%)
0 235
 
< 0.1%
499 37091
1.6%
504 8133
 
0.4%
509 8836
 
0.4%
514 10139
 
0.4%
ValueCountFrequency (%)
850 380
 
< 0.1%
844 874
 
< 0.1%
839 1708
 
0.1%
834 3459
0.2%
829 5614
0.2%

last_fico_range_low
Real number (ℝ)

Zeros 

Distinct71
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean675.5397298
Minimum0
Maximum845
Zeros37326
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:41.842239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile535
Q1650
median695
Q3730
95-th percentile790
Maximum845
Range845
Interquartile range (IQR)80

Descriptive statistics

Standard deviation111.0976261
Coefficient of variation (CV)0.1644575755
Kurtosis19.99158902
Mean675.5397298
Median Absolute Deviation (MAD)40
Skewness-3.737285082
Sum1527171050
Variance12342.68254
MonotonicityNot monotonic
2025-07-26T13:00:41.888918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
690 83697
 
3.7%
695 82915
 
3.7%
705 82358
 
3.6%
700 82319
 
3.6%
710 79410
 
3.5%
680 77821
 
3.4%
715 77448
 
3.4%
685 76599
 
3.4%
720 75229
 
3.3%
675 68535
 
3.0%
Other values (61) 1474337
65.2%
ValueCountFrequency (%)
0 37326
1.7%
500 8133
 
0.4%
505 8836
 
0.4%
510 10139
 
0.4%
515 10295
 
0.5%
ValueCountFrequency (%)
845 380
 
< 0.1%
840 874
 
< 0.1%
835 1708
 
0.1%
830 3459
0.2%
825 5614
0.2%

collections_12_mths_ex_med
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)< 0.1%
Missing178
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.01814580077
Minimum0
Maximum20
Zeros2223085
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:41.925378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1508131422
Coefficient of variation (CV)8.311186928
Kurtosis607.1260844
Mean0.01814580077
Median Absolute Deviation (MAD)0
Skewness14.03257434
Sum41019
Variance0.02274460386
MonotonicityNot monotonic
2025-07-26T13:00:41.964237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 2223085
98.3%
1 34684
 
1.5%
2 2313
 
0.1%
3 271
 
< 0.1%
4 93
 
< 0.1%
5 36
 
< 0.1%
6 17
 
< 0.1%
7 7
 
< 0.1%
8 4
 
< 0.1%
9 4
 
< 0.1%
Other values (6) 9
 
< 0.1%
(Missing) 178
 
< 0.1%
ValueCountFrequency (%)
0 2223085
98.3%
1 34684
 
1.5%
2 2313
 
0.1%
3 271
 
< 0.1%
4 93
 
< 0.1%
ValueCountFrequency (%)
20 2
< 0.1%
16 1
< 0.1%
14 1
< 0.1%
12 2
< 0.1%
11 1
< 0.1%

mths_since_last_major_derog
Real number (ℝ)

Missing 

Distinct183
Distinct (%)< 0.1%
Missing1679926
Missing (%)74.3%
Infinite0
Infinite (%)0.0%
Mean44.16422022
Minimum0
Maximum226
Zeros375
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:42.008499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q127
median44
Q362
95-th percentile77
Maximum226
Range226
Interquartile range (IQR)35

Descriptive statistics

Standard deviation21.53312059
Coefficient of variation (CV)0.4875693601
Kurtosis-0.5492963164
Mean44.16422022
Median Absolute Deviation (MAD)17
Skewness0.09211592216
Sum25649475
Variance463.6752825
MonotonicityNot monotonic
2025-07-26T13:00:42.141225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 9181
 
0.4%
43 9126
 
0.4%
42 9096
 
0.4%
44 9082
 
0.4%
46 9042
 
0.4%
48 8978
 
0.4%
40 8962
 
0.4%
41 8881
 
0.4%
47 8866
 
0.4%
38 8864
 
0.4%
Other values (173) 490697
 
21.7%
(Missing) 1679926
74.3%
ValueCountFrequency (%)
0 375
 
< 0.1%
1 1338
0.1%
2 1575
0.1%
3 1968
0.1%
4 2632
0.1%
ValueCountFrequency (%)
226 1
< 0.1%
202 1
< 0.1%
197 1
< 0.1%
195 1
< 0.1%
192 1
< 0.1%

policy_code
Real number (ℝ)

Constant 

Distinct1
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:42.194031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum2260668
Variance0
MonotonicityIncreasing
2025-07-26T13:00:42.225859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 2260668
> 99.9%
(Missing) 33
 
< 0.1%
ValueCountFrequency (%)
1 2260668
> 99.9%
ValueCountFrequency (%)
1 2260668
> 99.9%
Distinct2
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size127.1 MiB
2025-07-26T13:00:42.282529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.946604278
Min length9

Characters and Unicode

Total characters22485970
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowJoint App
4th rowIndividual
5th rowIndividual
ValueCountFrequency (%)
individual 2139958
89.9%
joint 120710
 
5.1%
app 120710
 
5.1%
2025-07-26T13:00:42.375232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 4400626
19.6%
d 4279916
19.0%
n 2260668
10.1%
I 2139958
9.5%
v 2139958
9.5%
u 2139958
9.5%
a 2139958
9.5%
l 2139958
9.5%
p 241420
 
1.1%
J 120710
 
0.5%
Other values (4) 482840
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22485970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4400626
19.6%
d 4279916
19.0%
n 2260668
10.1%
I 2139958
9.5%
v 2139958
9.5%
u 2139958
9.5%
a 2139958
9.5%
l 2139958
9.5%
p 241420
 
1.1%
J 120710
 
0.5%
Other values (4) 482840
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22485970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4400626
19.6%
d 4279916
19.0%
n 2260668
10.1%
I 2139958
9.5%
v 2139958
9.5%
u 2139958
9.5%
a 2139958
9.5%
l 2139958
9.5%
p 241420
 
1.1%
J 120710
 
0.5%
Other values (4) 482840
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22485970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4400626
19.6%
d 4279916
19.0%
n 2260668
10.1%
I 2139958
9.5%
v 2139958
9.5%
u 2139958
9.5%
a 2139958
9.5%
l 2139958
9.5%
p 241420
 
1.1%
J 120710
 
0.5%
Other values (4) 482840
 
2.1%

annual_inc_joint
Real number (ℝ)

Missing  Skewed 

Distinct17633
Distinct (%)14.6%
Missing2139991
Missing (%)94.7%
Infinite0
Infinite (%)0.0%
Mean123624.6367
Minimum5693.51
Maximum7874821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:42.437138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5693.51
5-th percentile53310.093
Q183400
median110000
Q3147995
95-th percentile230975
Maximum7874821
Range7869127.49
Interquartile range (IQR)64595

Descriptive statistics

Standard deviation74161.34633
Coefficient of variation (CV)0.5998913186
Kurtosis1741.838783
Mean123624.6367
Median Absolute Deviation (MAD)30000
Skewness21.7445355
Sum1.49227299 × 1010
Variance5499905289
MonotonicityNot monotonic
2025-07-26T13:00:42.496776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 2150
 
0.1%
120000 2057
 
0.1%
110000 2053
 
0.1%
90000 1874
 
0.1%
130000 1788
 
0.1%
80000 1650
 
0.1%
105000 1550
 
0.1%
140000 1529
 
0.1%
150000 1460
 
0.1%
115000 1460
 
0.1%
Other values (17623) 103139
 
4.6%
(Missing) 2139991
94.7%
ValueCountFrequency (%)
5693.51 1
< 0.1%
9000 1
< 0.1%
11000 1
< 0.1%
12500 1
< 0.1%
13464 1
< 0.1%
ValueCountFrequency (%)
7874821 1
< 0.1%
6282000 1
< 0.1%
5653500 1
< 0.1%
4200000 1
< 0.1%
2000000 1
< 0.1%

dti_joint
Real number (ℝ)

Missing 

Distinct4018
Distinct (%)3.3%
Missing2139995
Missing (%)94.7%
Infinite0
Infinite (%)0.0%
Mean19.25181706
Minimum0
Maximum69.49
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:42.568827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.99
Q113.53
median18.84
Q324.62
95-th percentile33.0275
Maximum69.49
Range69.49
Interquartile range (IQR)11.09

Descriptive statistics

Standard deviation7.82208598
Coefficient of variation (CV)0.4063037767
Kurtosis-0.3840496448
Mean19.25181706
Median Absolute Deviation (MAD)5.54
Skewness0.2205511496
Sum2323809.83
Variance61.18502907
MonotonicityNot monotonic
2025-07-26T13:00:42.617748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.32 80
 
< 0.1%
22.39 77
 
< 0.1%
18.1 77
 
< 0.1%
19.89 77
 
< 0.1%
17.96 76
 
< 0.1%
13.98 76
 
< 0.1%
18.96 76
 
< 0.1%
16.97 75
 
< 0.1%
17.02 75
 
< 0.1%
20.45 75
 
< 0.1%
Other values (4008) 119942
 
5.3%
(Missing) 2139995
94.7%
ValueCountFrequency (%)
0 18
< 0.1%
0.03 1
 
< 0.1%
0.11 1
 
< 0.1%
0.12 1
 
< 0.1%
0.13 2
 
< 0.1%
ValueCountFrequency (%)
69.49 1
< 0.1%
63.66 1
< 0.1%
61.9 1
< 0.1%
61.28 1
< 0.1%
55.52 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing2144971
Missing (%)94.9%
Memory size72.2 MiB
2025-07-26T13:00:42.660749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length12
Mean length12.09056424
Min length8

Characters and Unicode

Total characters1399241
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowNot Verified
3rd rowNot Verified
4th rowNot Verified
5th rowNot Verified
ValueCountFrequency (%)
verified 115730
55.7%
not 57403
27.6%
source 34827
 
16.7%
2025-07-26T13:00:42.764612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 266287
19.0%
i 231460
16.5%
r 150557
10.8%
V 115730
8.3%
f 115730
8.3%
d 115730
8.3%
o 92230
 
6.6%
92230
 
6.6%
N 57403
 
4.1%
t 57403
 
4.1%
Other values (3) 104481
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1399241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 266287
19.0%
i 231460
16.5%
r 150557
10.8%
V 115730
8.3%
f 115730
8.3%
d 115730
8.3%
o 92230
 
6.6%
92230
 
6.6%
N 57403
 
4.1%
t 57403
 
4.1%
Other values (3) 104481
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1399241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 266287
19.0%
i 231460
16.5%
r 150557
10.8%
V 115730
8.3%
f 115730
8.3%
d 115730
8.3%
o 92230
 
6.6%
92230
 
6.6%
N 57403
 
4.1%
t 57403
 
4.1%
Other values (3) 104481
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1399241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 266287
19.0%
i 231460
16.5%
r 150557
10.8%
V 115730
8.3%
f 115730
8.3%
d 115730
8.3%
o 92230
 
6.6%
92230
 
6.6%
N 57403
 
4.1%
t 57403
 
4.1%
Other values (3) 104481
 
7.5%

acc_now_delinq
Real number (ℝ)

Skewed  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing62
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.004147942241
Minimum0
Maximum14
Zeros2251857
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:42.812910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06961656266
Coefficient of variation (CV)16.78339731
Kurtosis1256.702032
Mean0.004147942241
Median Absolute Deviation (MAD)0
Skewness22.90797767
Sum9377
Variance0.004846465797
MonotonicityNot monotonic
2025-07-26T13:00:42.843377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2251857
99.6%
1 8293
 
0.4%
2 421
 
< 0.1%
3 50
 
< 0.1%
4 11
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
14 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 62
 
< 0.1%
ValueCountFrequency (%)
0 2251857
99.6%
1 8293
 
0.4%
2 421
 
< 0.1%
3 50
 
< 0.1%
4 11
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
< 0.1%
5 3
 
< 0.1%
4 11
< 0.1%

tot_coll_amt
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct15574
Distinct (%)0.7%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean232.7317389
Minimum0
Maximum9152545
Zeros1856129
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:42.886883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile786
Maximum9152545
Range9152545
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8518.461819
Coefficient of variation (CV)36.60206322
Kurtosis803765.2461
Mean232.7317389
Median Absolute Deviation (MAD)0
Skewness852.0101323
Sum509773739
Variance72564191.77
MonotonicityNot monotonic
2025-07-26T13:00:42.959802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1856129
82.1%
50 3924
 
0.2%
100 3250
 
0.1%
75 2489
 
0.1%
200 1911
 
0.1%
150 1904
 
0.1%
60 1791
 
0.1%
70 1457
 
0.1%
80 1411
 
0.1%
55 1247
 
0.1%
Other values (15564) 314879
 
13.9%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 1856129
82.1%
2 5
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
9152545 1
< 0.1%
6214661 1
< 0.1%
5252395 1
< 0.1%
932461 1
< 0.1%
848438 1
< 0.1%

tot_cur_bal
Real number (ℝ)

Missing 

Distinct487688
Distinct (%)22.3%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean142492.1952
Minimum0
Maximum9971659
Zeros959
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:43.043264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8195.55
Q129092
median79240
Q3213204
95-th percentile441856.45
Maximum9971659
Range9971659
Interquartile range (IQR)184112

Descriptive statistics

Standard deviation160692.6406
Coefficient of variation (CV)1.12772942
Kurtosis33.33453308
Mean142492.1952
Median Absolute Deviation (MAD)63035
Skewness2.974725283
Sum3.121137644 × 1011
Variance2.582212475 × 1010
MonotonicityNot monotonic
2025-07-26T13:00:43.108539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 959
 
< 0.1%
14186 41
 
< 0.1%
23772 39
 
< 0.1%
20275 39
 
< 0.1%
22831 38
 
< 0.1%
20317 38
 
< 0.1%
25197 38
 
< 0.1%
23607 38
 
< 0.1%
20157 38
 
< 0.1%
19923 38
 
< 0.1%
Other values (487678) 2189086
96.8%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 959
< 0.1%
1 13
 
< 0.1%
2 15
 
< 0.1%
3 19
 
< 0.1%
4 12
 
< 0.1%
ValueCountFrequency (%)
9971659 1
< 0.1%
8524709 1
< 0.1%
8000078 1
< 0.1%
5752177 1
< 0.1%
5445012 1
< 0.1%

open_acc_6m
Real number (ℝ)

Missing  Zeros 

Distinct19
Distinct (%)< 0.1%
Missing866163
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean0.934419858
Minimum0
Maximum18
Zeros627966
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.096173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.140699932
Coefficient of variation (CV)1.220757374
Kurtosis4.480970574
Mean0.934419858
Median Absolute Deviation (MAD)1
Skewness1.681378599
Sum1303084
Variance1.301196335
MonotonicityNot monotonic
2025-07-26T13:00:44.129771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 627966
27.8%
1 434092
19.2%
2 204167
 
9.0%
3 81147
 
3.6%
4 29906
 
1.3%
5 10643
 
0.5%
6 3956
 
0.2%
7 1556
 
0.1%
8 625
 
< 0.1%
9 259
 
< 0.1%
Other values (9) 221
 
< 0.1%
(Missing) 866163
38.3%
ValueCountFrequency (%)
0 627966
27.8%
1 434092
19.2%
2 204167
 
9.0%
3 81147
 
3.6%
4 29906
 
1.3%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 1
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
14 9
< 0.1%

open_act_il
Real number (ℝ)

Missing  Zeros 

Distinct54
Distinct (%)< 0.1%
Missing866162
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean2.779406671
Minimum0
Maximum57
Zeros165848
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.172681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile9
Maximum57
Range57
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.000784358
Coefficient of variation (CV)1.079649261
Kurtosis13.71859618
Mean2.779406671
Median Absolute Deviation (MAD)1
Skewness2.976152472
Sum3875991
Variance9.004706761
MonotonicityNot monotonic
2025-07-26T13:00:44.218311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 356302
15.8%
2 334086
 
14.8%
3 209677
 
9.3%
0 165848
 
7.3%
4 112592
 
5.0%
5 62357
 
2.8%
6 37624
 
1.7%
7 25961
 
1.1%
8 19050
 
0.8%
9 14849
 
0.7%
Other values (44) 56193
 
2.5%
(Missing) 866162
38.3%
ValueCountFrequency (%)
0 165848
7.3%
1 356302
15.8%
2 334086
14.8%
3 209677
9.3%
4 112592
 
5.0%
ValueCountFrequency (%)
57 1
< 0.1%
56 1
< 0.1%
55 1
< 0.1%
53 1
< 0.1%
49 2
< 0.1%

open_il_12m
Real number (ℝ)

Missing  Zeros 

Distinct19
Distinct (%)< 0.1%
Missing866162
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean0.6764314229
Minimum0
Maximum25
Zeros760254
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.253296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum25
Range25
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.925635427
Coefficient of variation (CV)1.368409858
Kurtosis5.428570249
Mean0.6764314229
Median Absolute Deviation (MAD)0
Skewness1.791122671
Sum943310
Variance0.8568009438
MonotonicityNot monotonic
2025-07-26T13:00:44.286592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 760254
33.6%
1 418123
18.5%
2 152164
 
6.7%
3 44170
 
2.0%
4 13608
 
0.6%
5 4309
 
0.2%
6 1484
 
0.1%
7 219
 
< 0.1%
8 97
 
< 0.1%
9 49
 
< 0.1%
Other values (9) 62
 
< 0.1%
(Missing) 866162
38.3%
ValueCountFrequency (%)
0 760254
33.6%
1 418123
18.5%
2 152164
 
6.7%
3 44170
 
2.0%
4 13608
 
0.6%
ValueCountFrequency (%)
25 1
< 0.1%
21 1
< 0.1%
20 2
< 0.1%
15 1
< 0.1%
14 1
< 0.1%

open_il_24m
Real number (ℝ)

Missing  Zeros 

Distinct31
Distinct (%)< 0.1%
Missing866162
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean1.562751562
Minimum0
Maximum51
Zeros377489
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.321794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum51
Range51
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.578672095
Coefficient of variation (CV)1.010187501
Kurtosis6.489226867
Mean1.562751562
Median Absolute Deviation (MAD)1
Skewness1.760411311
Sum2179318
Variance2.492205585
MonotonicityNot monotonic
2025-07-26T13:00:44.362194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 439811
19.5%
0 377489
16.7%
2 284333
 
12.6%
3 148127
 
6.6%
4 72627
 
3.2%
5 36227
 
1.6%
6 17577
 
0.8%
7 8851
 
0.4%
8 4506
 
0.2%
9 2262
 
0.1%
Other values (21) 2729
 
0.1%
(Missing) 866162
38.3%
ValueCountFrequency (%)
0 377489
16.7%
1 439811
19.5%
2 284333
12.6%
3 148127
 
6.6%
4 72627
 
3.2%
ValueCountFrequency (%)
51 1
< 0.1%
39 1
< 0.1%
31 1
< 0.1%
30 1
< 0.1%
28 1
< 0.1%

mths_since_rcnt_il
Real number (ℝ)

Missing 

Distinct405
Distinct (%)< 0.1%
Missing909957
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean21.22235672
Minimum0
Maximum511
Zeros653
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.414601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median13
Q324
95-th percentile72
Maximum511
Range511
Interquartile range (IQR)17

Descriptive statistics

Standard deviation26.0491867
Coefficient of variation (CV)1.227440809
Kurtosis17.41971902
Mean21.22235672
Median Absolute Deviation (MAD)8
Skewness3.432616292
Sum28665971
Variance678.5601279
MonotonicityNot monotonic
2025-07-26T13:00:44.458941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 61397
 
2.7%
4 60564
 
2.7%
3 58975
 
2.6%
6 57977
 
2.6%
8 57851
 
2.6%
5 57489
 
2.5%
9 53488
 
2.4%
13 51374
 
2.3%
10 51180
 
2.3%
2 50555
 
2.2%
Other values (395) 789894
34.9%
(Missing) 909957
40.3%
ValueCountFrequency (%)
0 653
 
< 0.1%
1 27716
1.2%
2 50555
2.2%
3 58975
2.6%
4 60564
2.7%
ValueCountFrequency (%)
511 1
< 0.1%
507 1
< 0.1%
505 1
< 0.1%
503 1
< 0.1%
488 1
< 0.1%

total_bal_il
Real number (ℝ)

Missing  Zeros 

Distinct162249
Distinct (%)11.6%
Missing866162
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean35506.64527
Minimum0
Maximum1837038
Zeros158666
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.505889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18695
median23127
Q346095
95-th percentile113501.1
Maximum1837038
Range1837038
Interquartile range (IQR)37400

Descriptive statistics

Standard deviation44097.45592
Coefficient of variation (CV)1.241949376
Kurtosis32.73888123
Mean35506.64527
Median Absolute Deviation (MAD)17047
Skewness3.821058228
Sum4.951540158 × 1010
Variance1944585619
MonotonicityNot monotonic
2025-07-26T13:00:44.549143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 158666
 
7.0%
10000 103
 
< 0.1%
15000 95
 
< 0.1%
5000 86
 
< 0.1%
5500 77
 
< 0.1%
9500 66
 
< 0.1%
2000 65
 
< 0.1%
6000 64
 
< 0.1%
7000 64
 
< 0.1%
20000 64
 
< 0.1%
Other values (162239) 1235189
54.6%
(Missing) 866162
38.3%
ValueCountFrequency (%)
0 158666
7.0%
1 58
 
< 0.1%
2 12
 
< 0.1%
3 12
 
< 0.1%
4 9
 
< 0.1%
ValueCountFrequency (%)
1837038 1
< 0.1%
1754743 1
< 0.1%
1711009 1
< 0.1%
1547285 1
< 0.1%
1466398 1
< 0.1%

il_util
Real number (ℝ)

Missing 

Distinct280
Distinct (%)< 0.1%
Missing1068883
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean69.14097958
Minimum0
Maximum1000
Zeros6629
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.599461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q155
median72
Q386
95-th percentile101
Maximum1000
Range1000
Interquartile range (IQR)31

Descriptive statistics

Standard deviation23.74838634
Coefficient of variation (CV)0.3434777246
Kurtosis7.081786181
Mean69.14097958
Median Absolute Deviation (MAD)15
Skewness-0.1724200621
Sum82403464
Variance563.9858538
MonotonicityNot monotonic
2025-07-26T13:00:44.646951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78 22659
 
1.0%
75 22425
 
1.0%
81 22330
 
1.0%
83 22286
 
1.0%
72 22023
 
1.0%
77 21852
 
1.0%
80 21737
 
1.0%
82 21553
 
1.0%
74 21496
 
1.0%
79 21478
 
1.0%
Other values (270) 971979
43.0%
(Missing) 1068883
47.3%
ValueCountFrequency (%)
0 6629
0.3%
1 439
 
< 0.1%
2 886
 
< 0.1%
3 1761
 
0.1%
4 1112
 
< 0.1%
ValueCountFrequency (%)
1000 3
< 0.1%
558 1
 
< 0.1%
464 1
 
< 0.1%
428 1
 
< 0.1%
417 1
 
< 0.1%

open_rv_12m
Real number (ℝ)

Missing  Zeros 

Distinct29
Distinct (%)< 0.1%
Missing866162
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean1.290133155
Minimum0
Maximum28
Zeros513716
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.684626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum28
Range28
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.506826647
Coefficient of variation (CV)1.167962114
Kurtosis7.794958019
Mean1.290133155
Median Absolute Deviation (MAD)1
Skewness2.019059937
Sum1799141
Variance2.270526544
MonotonicityNot monotonic
2025-07-26T13:00:44.720213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 513716
22.7%
1 413897
18.3%
2 238188
 
10.5%
3 118920
 
5.3%
4 56137
 
2.5%
5 26685
 
1.2%
6 13085
 
0.6%
7 6466
 
0.3%
8 3218
 
0.1%
9 1778
 
0.1%
Other values (19) 2449
 
0.1%
(Missing) 866162
38.3%
ValueCountFrequency (%)
0 513716
22.7%
1 413897
18.3%
2 238188
10.5%
3 118920
 
5.3%
4 56137
 
2.5%
ValueCountFrequency (%)
28 2
 
< 0.1%
27 1
 
< 0.1%
26 5
< 0.1%
25 1
 
< 0.1%
24 2
 
< 0.1%

open_rv_24m
Real number (ℝ)

Missing  Zeros 

Distinct50
Distinct (%)< 0.1%
Missing866162
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean2.749923093
Minimum0
Maximum60
Zeros223783
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.763956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum60
Range60
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.596910679
Coefficient of variation (CV)0.9443575663
Kurtosis8.208281738
Mean2.749923093
Median Absolute Deviation (MAD)1
Skewness1.977657369
Sum3834875
Variance6.743945077
MonotonicityNot monotonic
2025-07-26T13:00:44.810809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 297887
 
13.2%
2 267192
 
11.8%
0 223783
 
9.9%
3 200886
 
8.9%
4 139066
 
6.2%
5 92969
 
4.1%
6 60078
 
2.7%
7 39084
 
1.7%
8 24997
 
1.1%
9 16162
 
0.7%
Other values (40) 32435
 
1.4%
(Missing) 866162
38.3%
ValueCountFrequency (%)
0 223783
9.9%
1 297887
13.2%
2 267192
11.8%
3 200886
8.9%
4 139066
6.2%
ValueCountFrequency (%)
60 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%
50 2
< 0.1%
49 1
< 0.1%

max_bal_bc
Real number (ℝ)

Missing  Zeros 

Distinct33726
Distinct (%)2.4%
Missing866162
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean5806.392905
Minimum0
Maximum1170668
Zeros35917
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.857450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile340
Q12284
median4413
Q37598
95-th percentile16311
Maximum1170668
Range1170668
Interquartile range (IQR)5314

Descriptive statistics

Standard deviation5690.561012
Coefficient of variation (CV)0.9800509723
Kurtosis1748.219344
Mean5806.392905
Median Absolute Deviation (MAD)2462
Skewness13.69539884
Sum8097241355
Variance32382484.63
MonotonicityNot monotonic
2025-07-26T13:00:44.910788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35917
 
1.6%
8 572
 
< 0.1%
3000 533
 
< 0.1%
2000 504
 
< 0.1%
4000 447
 
< 0.1%
5000 423
 
< 0.1%
2500 383
 
< 0.1%
1500 333
 
< 0.1%
1900 333
 
< 0.1%
3500 330
 
< 0.1%
Other values (33716) 1354764
59.9%
(Missing) 866162
38.3%
ValueCountFrequency (%)
0 35917
1.6%
1 165
 
< 0.1%
2 218
 
< 0.1%
3 234
 
< 0.1%
4 217
 
< 0.1%
ValueCountFrequency (%)
1170668 1
< 0.1%
776843 1
< 0.1%
571793 1
< 0.1%
500000 1
< 0.1%
457521 1
< 0.1%

all_util
Real number (ℝ)

Missing 

Distinct188
Distinct (%)< 0.1%
Missing866381
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean57.03229531
Minimum0
Maximum239
Zeros2873
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:44.961208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q143
median58
Q372
95-th percentile90
Maximum239
Range239
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.9047476
Coefficient of variation (CV)0.3665422807
Kurtosis-0.07732273667
Mean57.03229531
Median Absolute Deviation (MAD)14
Skewness-0.1233140004
Sum79521270
Variance437.0084721
MonotonicityNot monotonic
2025-07-26T13:00:45.013194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 26677
 
1.2%
60 26674
 
1.2%
61 26475
 
1.2%
62 26330
 
1.2%
63 26295
 
1.2%
58 26105
 
1.2%
64 26010
 
1.2%
57 25934
 
1.1%
65 25763
 
1.1%
56 25429
 
1.1%
Other values (178) 1132628
50.1%
(Missing) 866381
38.3%
ValueCountFrequency (%)
0 2873
0.1%
1 1720
0.1%
2 1594
0.1%
3 1676
0.1%
4 1706
0.1%
ValueCountFrequency (%)
239 1
< 0.1%
211 1
< 0.1%
210 1
< 0.1%
204 1
< 0.1%
198 1
< 0.1%

total_rev_hi_lim
Real number (ℝ)

Missing  Skewed 

Distinct34220
Distinct (%)1.6%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean34573.94277
Minimum0
Maximum9999999
Zeros1366
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:45.060144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6000
Q114700
median25400
Q343200
95-th percentile91100
Maximum9999999
Range9999999
Interquartile range (IQR)28500

Descriptive statistics

Standard deviation36728.49545
Coefficient of variation (CV)1.06231724
Kurtosis7520.926598
Mean34573.94277
Median Absolute Deviation (MAD)12800
Skewness32.55742738
Sum7.573048765 × 1010
Variance1348982378
MonotonicityNot monotonic
2025-07-26T13:00:45.113948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 7086
 
0.3%
15000 6840
 
0.3%
13000 6720
 
0.3%
12000 6657
 
0.3%
14000 6640
 
0.3%
11000 6624
 
0.3%
11500 6591
 
0.3%
16000 6535
 
0.3%
17000 6526
 
0.3%
12500 6496
 
0.3%
Other values (34210) 2123677
93.9%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 1366
0.1%
100 28
 
< 0.1%
200 71
 
< 0.1%
300 354
 
< 0.1%
400 113
 
< 0.1%
ValueCountFrequency (%)
9999999 3
< 0.1%
2175000 1
 
< 0.1%
2087500 1
 
< 0.1%
2059200 1
 
< 0.1%
2013133 1
 
< 0.1%

inq_fi
Real number (ℝ)

Missing  Zeros 

Distinct33
Distinct (%)< 0.1%
Missing866162
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean1.012866618
Minimum0
Maximum48
Zeros697142
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:45.155354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum48
Range48
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.489455668
Coefficient of variation (CV)1.470534858
Kurtosis13.46503817
Mean1.012866618
Median Absolute Deviation (MAD)1
Skewness2.665084535
Sum1412482
Variance2.218478187
MonotonicityNot monotonic
2025-07-26T13:00:45.199570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 697142
30.8%
1 352169
15.6%
2 172982
 
7.7%
3 83887
 
3.7%
4 41460
 
1.8%
5 21374
 
0.9%
6 11043
 
0.5%
7 6059
 
0.3%
8 3423
 
0.2%
9 1946
 
0.1%
Other values (23) 3054
 
0.1%
(Missing) 866162
38.3%
ValueCountFrequency (%)
0 697142
30.8%
1 352169
15.6%
2 172982
 
7.7%
3 83887
 
3.7%
4 41460
 
1.8%
ValueCountFrequency (%)
48 1
< 0.1%
38 1
< 0.1%
32 1
< 0.1%
31 1
< 0.1%
29 1
< 0.1%

total_cu_tl
Real number (ℝ)

Missing  Zeros 

Distinct62
Distinct (%)< 0.1%
Missing866163
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean1.477304312
Minimum0
Maximum111
Zeros753128
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:45.248988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7
Maximum111
Range111
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.672991191
Coefficient of variation (CV)1.809370737
Kurtosis24.10976388
Mean1.477304312
Median Absolute Deviation (MAD)0
Skewness3.575038998
Sum2060157
Variance7.144881908
MonotonicityNot monotonic
2025-07-26T13:00:45.300868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 753128
33.3%
1 235619
 
10.4%
2 129673
 
5.7%
3 82042
 
3.6%
4 55090
 
2.4%
5 38010
 
1.7%
6 27101
 
1.2%
7 19362
 
0.9%
8 13953
 
0.6%
9 10143
 
0.4%
Other values (52) 30417
 
1.3%
(Missing) 866163
38.3%
ValueCountFrequency (%)
0 753128
33.3%
1 235619
 
10.4%
2 129673
 
5.7%
3 82042
 
3.6%
4 55090
 
2.4%
ValueCountFrequency (%)
111 1
 
< 0.1%
79 1
 
< 0.1%
77 1
 
< 0.1%
71 1
 
< 0.1%
68 3
< 0.1%

inq_last_12m
Real number (ℝ)

Missing  Zeros 

Distinct48
Distinct (%)< 0.1%
Missing866163
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean2.036667341
Minimum0
Maximum67
Zeros400090
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:45.344396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum67
Range67
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.383117224
Coefficient of variation (CV)1.170106269
Kurtosis11.22465969
Mean2.036667341
Median Absolute Deviation (MAD)1
Skewness2.405157988
Sum2840210
Variance5.679247702
MonotonicityNot monotonic
2025-07-26T13:00:45.390071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 400090
17.7%
1 339411
 
15.0%
2 235736
 
10.4%
3 153565
 
6.8%
4 96535
 
4.3%
5 59861
 
2.6%
6 37765
 
1.7%
7 24088
 
1.1%
8 15619
 
0.7%
9 10201
 
0.5%
Other values (38) 21667
 
1.0%
(Missing) 866163
38.3%
ValueCountFrequency (%)
0 400090
17.7%
1 339411
15.0%
2 235736
10.4%
3 153565
 
6.8%
4 96535
 
4.3%
ValueCountFrequency (%)
67 1
< 0.1%
51 1
< 0.1%
49 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%

acc_open_past_24mths
Real number (ℝ)

Missing  Zeros 

Distinct57
Distinct (%)< 0.1%
Missing50063
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean4.521655739
Minimum0
Maximum64
Zeros100270
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:45.442885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum64
Range64
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.164229436
Coefficient of variation (CV)0.6997944157
Kurtosis4.374804415
Mean4.521655739
Median Absolute Deviation (MAD)2
Skewness1.402234451
Sum9995744
Variance10.01234792
MonotonicityNot monotonic
2025-07-26T13:00:45.498858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 331293
14.7%
4 307313
13.6%
2 305502
13.5%
5 258117
11.4%
1 222952
9.9%
6 201931
8.9%
7 149447
6.6%
8 106194
 
4.7%
0 100270
 
4.4%
9 73503
 
3.3%
Other values (47) 154116
6.8%
ValueCountFrequency (%)
0 100270
 
4.4%
1 222952
9.9%
2 305502
13.5%
3 331293
14.7%
4 307313
13.6%
ValueCountFrequency (%)
64 1
 
< 0.1%
61 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
54 3
< 0.1%

avg_cur_bal
Real number (ℝ)

Missing 

Distinct88597
Distinct (%)4.0%
Missing70379
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean13547.79751
Minimum0
Maximum958084
Zeros906
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:45.548847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1072
Q13080
median7335
Q318783
95-th percentile43578
Maximum958084
Range958084
Interquartile range (IQR)15703

Descriptive statistics

Standard deviation16474.07501
Coefficient of variation (CV)1.215996548
Kurtosis44.16460012
Mean13547.79751
Median Absolute Deviation (MAD)5369
Skewness3.868891119
Sum2.967403894 × 1010
Variance271395147.4
MonotonicityNot monotonic
2025-07-26T13:00:45.601532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 906
 
< 0.1%
2442 273
 
< 0.1%
2277 271
 
< 0.1%
1971 268
 
< 0.1%
2522 267
 
< 0.1%
1967 267
 
< 0.1%
2148 266
 
< 0.1%
2025 266
 
< 0.1%
2831 265
 
< 0.1%
2286 265
 
< 0.1%
Other values (88587) 2187008
96.7%
(Missing) 70379
 
3.1%
ValueCountFrequency (%)
0 906
< 0.1%
1 67
 
< 0.1%
2 59
 
< 0.1%
3 50
 
< 0.1%
4 38
 
< 0.1%
ValueCountFrequency (%)
958084 1
< 0.1%
800008 1
< 0.1%
752994 1
< 0.1%
710392 1
< 0.1%
646339 1
< 0.1%

bc_open_to_buy
Real number (ℝ)

Missing  Zeros 

Distinct91500
Distinct (%)4.2%
Missing74968
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean11394.26269
Minimum0
Maximum711140
Zeros30767
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:45.658088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile162
Q11722
median5442
Q314187
95-th percentile42800
Maximum711140
Range711140
Interquartile range (IQR)12465

Descriptive statistics

Standard deviation16599.5344
Coefficient of variation (CV)1.456832693
Kurtosis27.09912245
Mean11394.26269
Median Absolute Deviation (MAD)4567
Skewness3.737088307
Sum2.490481597 × 1010
Variance275544542.3
MonotonicityNot monotonic
2025-07-26T13:00:45.716707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30767
 
1.4%
500 1954
 
0.1%
2000 1908
 
0.1%
1000 1709
 
0.1%
3000 1580
 
0.1%
2500 1395
 
0.1%
5000 1383
 
0.1%
1500 1300
 
0.1%
4000 1234
 
0.1%
3500 1160
 
0.1%
Other values (91490) 2141343
94.7%
(Missing) 74968
 
3.3%
ValueCountFrequency (%)
0 30767
1.4%
1 342
 
< 0.1%
2 354
 
< 0.1%
3 358
 
< 0.1%
4 384
 
< 0.1%
ValueCountFrequency (%)
711140 1
< 0.1%
605996 1
< 0.1%
559912 1
< 0.1%
507259 1
< 0.1%
497445 1
< 0.1%

bc_util
Real number (ℝ)

Missing  Zeros 

Distinct1494
Distinct (%)0.1%
Missing76104
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean57.89994754
Minimum0
Maximum339.6
Zeros27885
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:45.770491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.7
Q135.4
median60.2
Q383.1
95-th percentile97.8
Maximum339.6
Range339.6
Interquartile range (IQR)47.7

Descriptive statistics

Standard deviation28.58347454
Coefficient of variation (CV)0.4936701284
Kurtosis-1.001197473
Mean57.89994754
Median Absolute Deviation (MAD)23.8
Skewness-0.2697763948
Sum126488051.7
Variance817.0150167
MonotonicityNot monotonic
2025-07-26T13:00:45.819943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27885
 
1.2%
98 6188
 
0.3%
97 5748
 
0.3%
99 5700
 
0.3%
96 5642
 
0.2%
95 5223
 
0.2%
94 4983
 
0.2%
93 4693
 
0.2%
92 4594
 
0.2%
91 4381
 
0.2%
Other values (1484) 2109560
93.3%
(Missing) 76104
 
3.4%
ValueCountFrequency (%)
0 27885
1.2%
0.1 2267
 
0.1%
0.2 1959
 
0.1%
0.3 1680
 
0.1%
0.4 1440
 
0.1%
ValueCountFrequency (%)
339.6 1
< 0.1%
318.2 1
< 0.1%
255.2 1
< 0.1%
252.3 1
< 0.1%
243.8 1
< 0.1%

chargeoff_within_12_mths
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing178
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.008464412881
Minimum0
Maximum10
Zeros2243339
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:45.858663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1048097892
Coefficient of variation (CV)12.38240509
Kurtosis598.1324648
Mean0.008464412881
Median Absolute Deviation (MAD)0
Skewness18.12854845
Sum19134
Variance0.01098509191
MonotonicityNot monotonic
2025-07-26T13:00:45.890134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 2243339
99.2%
1 15765
 
0.7%
2 1115
 
< 0.1%
3 186
 
< 0.1%
4 68
 
< 0.1%
5 22
 
< 0.1%
6 12
 
< 0.1%
7 8
 
< 0.1%
9 5
 
< 0.1%
8 2
 
< 0.1%
(Missing) 178
 
< 0.1%
ValueCountFrequency (%)
0 2243339
99.2%
1 15765
 
0.7%
2 1115
 
< 0.1%
3 186
 
< 0.1%
4 68
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 5
< 0.1%
8 2
 
< 0.1%
7 8
< 0.1%
6 12
< 0.1%

delinq_amnt
Real number (ℝ)

Skewed  Zeros 

Distinct2617
Distinct (%)0.1%
Missing62
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.36982773
Minimum0
Maximum249925
Zeros2253465
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:45.943315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum249925
Range249925
Interquartile range (IQR)0

Descriptive statistics

Standard deviation726.4647813
Coefficient of variation (CV)58.72877108
Kurtosis16006.0037
Mean12.36982773
Median Absolute Deviation (MAD)0
Skewness102.6547743
Sum27963715
Variance527751.0785
MonotonicityNot monotonic
2025-07-26T13:00:45.991675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2253465
99.7%
25 124
 
< 0.1%
65000 109
 
< 0.1%
30 85
 
< 0.1%
53 72
 
< 0.1%
54 69
 
< 0.1%
75 65
 
< 0.1%
50 64
 
< 0.1%
56 58
 
< 0.1%
57 56
 
< 0.1%
Other values (2607) 6472
 
0.3%
(Missing) 62
 
< 0.1%
ValueCountFrequency (%)
0 2253465
99.7%
1 9
 
< 0.1%
2 10
 
< 0.1%
3 12
 
< 0.1%
4 15
 
< 0.1%
ValueCountFrequency (%)
249925 1
< 0.1%
185408 1
< 0.1%
159177 1
< 0.1%
138474 1
< 0.1%
130778 1
< 0.1%

mo_sin_old_il_acct
Real number (ℝ)

Missing 

Distinct566
Distinct (%)< 0.1%
Missing139104
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean125.7377608
Minimum0
Maximum999
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.037456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q196
median130
Q3154
95-th percentile213
Maximum999
Range999
Interquartile range (IQR)58

Descriptive statistics

Standard deviation53.38217542
Coefficient of variation (CV)0.4245516629
Kurtosis1.843977671
Mean125.7377608
Median Absolute Deviation (MAD)27
Skewness0.3513513484
Sum266764856
Variance2849.656652
MonotonicityNot monotonic
2025-07-26T13:00:46.081817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 25017
 
1.1%
129 24849
 
1.1%
128 24798
 
1.1%
132 24767
 
1.1%
127 24749
 
1.1%
133 24731
 
1.1%
125 24683
 
1.1%
126 24649
 
1.1%
134 24574
 
1.1%
131 24515
 
1.1%
Other values (556) 1874265
82.9%
(Missing) 139104
 
6.2%
ValueCountFrequency (%)
0 16
 
< 0.1%
1 507
 
< 0.1%
2 1003
< 0.1%
3 1414
0.1%
4 1616
0.1%
ValueCountFrequency (%)
999 2
< 0.1%
848 1
< 0.1%
827 1
< 0.1%
822 1
< 0.1%
808 1
< 0.1%

mo_sin_old_rev_tl_op
Real number (ℝ)

Missing 

Distinct787
Distinct (%)< 0.1%
Missing70310
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean181.4915675
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.123862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile49
Q1116
median164
Q3232
95-th percentile369
Maximum999
Range998
Interquartile range (IQR)116

Descriptive statistics

Standard deviation97.11845373
Coefficient of variation (CV)0.5351127608
Kurtosis1.355518547
Mean181.4915675
Median Absolute Deviation (MAD)56
Skewness1.007808058
Sum397537496
Variance9431.994056
MonotonicityNot monotonic
2025-07-26T13:00:46.167916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132 12536
 
0.6%
136 12528
 
0.6%
131 12386
 
0.5%
134 12317
 
0.5%
130 12299
 
0.5%
133 12278
 
0.5%
135 12235
 
0.5%
137 12132
 
0.5%
129 12116
 
0.5%
140 12112
 
0.5%
Other values (777) 2067452
91.5%
(Missing) 70310
 
3.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 6
 
< 0.1%
3 17
< 0.1%
4 19
< 0.1%
5 42
< 0.1%
ValueCountFrequency (%)
999 1
< 0.1%
901 1
< 0.1%
852 1
< 0.1%
851 1
< 0.1%
842 1
< 0.1%

mo_sin_rcnt_rev_tl_op
Real number (ℝ)

Missing  Zeros 

Distinct333
Distinct (%)< 0.1%
Missing70310
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean14.02408931
Minimum0
Maximum547
Zeros33747
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.213596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median8
Q317
95-th percentile46
Maximum547
Range547
Interquartile range (IQR)13

Descriptive statistics

Standard deviation17.53308255
Coefficient of variation (CV)1.250211844
Kurtosis21.96014259
Mean14.02408931
Median Absolute Deviation (MAD)5
Skewness3.592705204
Sum30718239
Variance307.4089838
MonotonicityNot monotonic
2025-07-26T13:00:46.257817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 167693
 
7.4%
3 161273
 
7.1%
4 147691
 
6.5%
1 137574
 
6.1%
5 132964
 
5.9%
6 117844
 
5.2%
7 107901
 
4.8%
8 96426
 
4.3%
9 85716
 
3.8%
10 78061
 
3.5%
Other values (323) 957248
42.3%
ValueCountFrequency (%)
0 33747
 
1.5%
1 137574
6.1%
2 167693
7.4%
3 161273
7.1%
4 147691
6.5%
ValueCountFrequency (%)
547 1
< 0.1%
502 1
< 0.1%
438 1
< 0.1%
406 1
< 0.1%
404 1
< 0.1%

mo_sin_rcnt_tl
Real number (ℝ)

Missing  Zeros 

Distinct232
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean8.297468672
Minimum0
Maximum382
Zeros34923
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.303556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q311
95-th percentile24
Maximum382
Range382
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.208556539
Coefficient of variation (CV)1.10980311
Kurtosis46.21937168
Mean8.297468672
Median Absolute Deviation (MAD)3
Skewness4.601065685
Sum18174709
Variance84.79751352
MonotonicityNot monotonic
2025-07-26T13:00:46.355069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 235575
10.4%
3 228153
 
10.1%
4 203278
 
9.0%
1 180626
 
8.0%
5 175501
 
7.8%
6 151978
 
6.7%
7 135550
 
6.0%
8 114936
 
5.1%
9 96422
 
4.3%
10 82385
 
3.6%
Other values (222) 585988
25.9%
ValueCountFrequency (%)
0 34923
 
1.5%
1 180626
8.0%
2 235575
10.4%
3 228153
10.1%
4 203278
9.0%
ValueCountFrequency (%)
382 1
< 0.1%
368 1
< 0.1%
353 1
< 0.1%
331 1
< 0.1%
314 1
< 0.1%

mort_acc
Real number (ℝ)

Missing  Zeros 

Distinct47
Distinct (%)< 0.1%
Missing50063
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1.555382202
Minimum0
Maximum94
Zeros929606
Zeros (%)41.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.408279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum94
Range94
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.904981017
Coefficient of variation (CV)1.224767144
Kurtosis10.50475648
Mean1.555382202
Median Absolute Deviation (MAD)1
Skewness1.789683755
Sum3438387
Variance3.628952676
MonotonicityNot monotonic
2025-07-26T13:00:46.450642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 929606
41.1%
1 393270
17.4%
2 325903
 
14.4%
3 231066
 
10.2%
4 150002
 
6.6%
5 86666
 
3.8%
6 46804
 
2.1%
7 23419
 
1.0%
8 11450
 
0.5%
9 5742
 
0.3%
Other values (37) 6710
 
0.3%
(Missing) 50063
 
2.2%
ValueCountFrequency (%)
0 929606
41.1%
1 393270
17.4%
2 325903
 
14.4%
3 231066
 
10.2%
4 150002
 
6.6%
ValueCountFrequency (%)
94 1
< 0.1%
87 1
< 0.1%
61 1
< 0.1%
52 1
< 0.1%
51 1
< 0.1%

mths_since_recent_bc
Real number (ℝ)

Missing 

Distinct546
Distinct (%)< 0.1%
Missing73445
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean24.84485081
Minimum0
Maximum661
Zeros13450
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.494438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median14
Q330
95-th percentile89
Maximum661
Range661
Interquartile range (IQR)24

Descriptive statistics

Standard deviation32.31925269
Coefficient of variation (CV)1.300843098
Kurtosis20.67203482
Mean24.84485081
Median Absolute Deviation (MAD)9
Skewness3.507711335
Sum54342049
Variance1044.534095
MonotonicityNot monotonic
2025-07-26T13:00:46.538739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 104912
 
4.6%
2 102518
 
4.5%
4 100529
 
4.4%
5 94879
 
4.2%
6 88025
 
3.9%
7 83796
 
3.7%
8 78467
 
3.5%
9 72912
 
3.2%
1 69999
 
3.1%
10 68865
 
3.0%
Other values (536) 1322354
58.5%
(Missing) 73445
 
3.2%
ValueCountFrequency (%)
0 13450
 
0.6%
1 69999
3.1%
2 102518
4.5%
3 104912
4.6%
4 100529
4.4%
ValueCountFrequency (%)
661 1
< 0.1%
656 1
< 0.1%
640 1
< 0.1%
639 1
< 0.1%
628 1
< 0.1%

mths_since_recent_bc_dlq
Real number (ℝ)

Missing 

Distinct177
Distinct (%)< 0.1%
Missing1741000
Missing (%)77.0%
Infinite0
Infinite (%)0.0%
Mean39.30308966
Minimum0
Maximum202
Zeros796
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.583444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q121
median37
Q357
95-th percentile77
Maximum202
Range202
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.61768864
Coefficient of variation (CV)0.575468464
Kurtosis-0.6228222102
Mean39.30308966
Median Absolute Deviation (MAD)18
Skewness0.3340775888
Sum20425855
Variance511.5598394
MonotonicityNot monotonic
2025-07-26T13:00:46.630527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 8060
 
0.4%
28 8027
 
0.4%
25 7934
 
0.4%
35 7933
 
0.4%
45 7906
 
0.3%
22 7903
 
0.3%
30 7895
 
0.3%
44 7887
 
0.3%
32 7883
 
0.3%
19 7875
 
0.3%
Other values (167) 440398
 
19.5%
(Missing) 1741000
77.0%
ValueCountFrequency (%)
0 796
 
< 0.1%
1 2629
0.1%
2 2998
0.1%
3 4142
0.2%
4 4816
0.2%
ValueCountFrequency (%)
202 1
< 0.1%
195 1
< 0.1%
194 1
< 0.1%
190 1
< 0.1%
189 1
< 0.1%

mths_since_recent_inq
Real number (ℝ)

Missing  Zeros 

Distinct26
Distinct (%)< 0.1%
Missing295468
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean7.024194078
Minimum0
Maximum25
Zeros168927
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.671186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q311
95-th percentile19
Maximum25
Range25
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.965411442
Coefficient of variation (CV)0.8492663181
Kurtosis-0.03946573685
Mean7.024194078
Median Absolute Deviation (MAD)4
Skewness0.8890161004
Sum13804178
Variance35.58613367
MonotonicityNot monotonic
2025-07-26T13:00:46.706460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 212773
 
9.4%
2 173553
 
7.7%
0 168927
 
7.5%
3 157363
 
7.0%
4 143887
 
6.4%
5 127796
 
5.7%
6 114793
 
5.1%
7 108912
 
4.8%
8 96554
 
4.3%
9 85028
 
3.8%
Other values (16) 575647
25.5%
(Missing) 295468
13.1%
ValueCountFrequency (%)
0 168927
7.5%
1 212773
9.4%
2 173553
7.7%
3 157363
7.0%
4 143887
6.4%
ValueCountFrequency (%)
25 31
 
< 0.1%
24 9247
0.4%
23 18516
0.8%
22 20110
0.9%
21 22085
1.0%

mths_since_recent_revol_delinq
Real number (ℝ)

Missing 

Distinct179
Distinct (%)< 0.1%
Missing1520342
Missing (%)67.3%
Infinite0
Infinite (%)0.0%
Mean35.78222322
Minimum0
Maximum202
Zeros1321
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.751803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q117
median33
Q351
95-th percentile76
Maximum202
Range202
Interquartile range (IQR)34

Descriptive statistics

Standard deviation22.30723894
Coefficient of variation (CV)0.6234167957
Kurtosis-0.486454921
Mean35.78222322
Median Absolute Deviation (MAD)17
Skewness0.4954874723
Sum26491691
Variance497.6129092
MonotonicityNot monotonic
2025-07-26T13:00:46.803653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 13402
 
0.6%
13 13281
 
0.6%
14 12802
 
0.6%
15 12782
 
0.6%
19 12542
 
0.6%
16 12506
 
0.6%
18 12500
 
0.6%
9 12431
 
0.5%
21 12324
 
0.5%
22 12269
 
0.5%
Other values (169) 613520
27.1%
(Missing) 1520342
67.3%
ValueCountFrequency (%)
0 1321
 
0.1%
1 4821
0.2%
2 5792
0.3%
3 7817
0.3%
4 9073
0.4%
ValueCountFrequency (%)
202 1
< 0.1%
197 1
< 0.1%
190 1
< 0.1%
188 1
< 0.1%
183 1
< 0.1%

num_accts_ever_120_pd
Real number (ℝ)

Missing  Zeros 

Distinct44
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean0.5002081819
Minimum0
Maximum58
Zeros1687416
Zeros (%)74.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.850345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.350325678
Coefficient of variation (CV)2.69952737
Kurtosis52.39404559
Mean0.5002081819
Median Absolute Deviation (MAD)0
Skewness5.434771953
Sum1095652
Variance1.823379435
MonotonicityNot monotonic
2025-07-26T13:00:46.898000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 1687416
74.6%
1 270277
 
12.0%
2 105854
 
4.7%
3 48574
 
2.1%
4 28665
 
1.3%
5 17014
 
0.8%
6 11075
 
0.5%
7 7023
 
0.3%
8 4555
 
0.2%
9 2975
 
0.1%
Other values (34) 6964
 
0.3%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 1687416
74.6%
1 270277
 
12.0%
2 105854
 
4.7%
3 48574
 
2.1%
4 28665
 
1.3%
ValueCountFrequency (%)
58 1
< 0.1%
51 1
< 0.1%
45 1
< 0.1%
42 2
< 0.1%
39 2
< 0.1%

num_actv_bc_tl
Real number (ℝ)

Missing  Zeros 

Distinct42
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean3.676068941
Minimum0
Maximum50
Zeros50061
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:46.942768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum50
Range50
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.324646415
Coefficient of variation (CV)0.6323729105
Kurtosis4.647915181
Mean3.676068941
Median Absolute Deviation (MAD)1
Skewness1.476436002
Sum8052032
Variance5.403980957
MonotonicityNot monotonic
2025-07-26T13:00:46.989737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3 458887
20.3%
2 442726
19.6%
4 357154
15.8%
1 256818
11.4%
5 240636
10.6%
6 152238
 
6.7%
7 91653
 
4.1%
8 55107
 
2.4%
0 50061
 
2.2%
9 33163
 
1.5%
Other values (32) 51949
 
2.3%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 50061
 
2.2%
1 256818
11.4%
2 442726
19.6%
3 458887
20.3%
4 357154
15.8%
ValueCountFrequency (%)
50 1
< 0.1%
48 2
< 0.1%
47 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%

num_actv_rev_tl
Real number (ℝ)

Missing 

Distinct57
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean5.629467693
Minimum0
Maximum72
Zeros11439
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.032921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q37
95-th percentile12
Maximum72
Range72
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.382873759
Coefficient of variation (CV)0.6009224927
Kurtosis4.996738255
Mean5.629467693
Median Absolute Deviation (MAD)2
Skewness1.573518347
Sum12330741
Variance11.44383487
MonotonicityNot monotonic
2025-07-26T13:00:47.082701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 333303
14.7%
3 308512
13.6%
5 303634
13.4%
6 248257
11.0%
2 213366
9.4%
7 191696
8.5%
8 141199
6.2%
9 102473
 
4.5%
1 84992
 
3.8%
10 72350
 
3.2%
Other values (47) 190610
8.4%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 11439
 
0.5%
1 84992
 
3.8%
2 213366
9.4%
3 308512
13.6%
4 333303
14.7%
ValueCountFrequency (%)
72 1
 
< 0.1%
63 1
 
< 0.1%
60 1
 
< 0.1%
59 3
< 0.1%
57 2
< 0.1%

num_bc_sats
Real number (ℝ)

Missing  Zeros 

Distinct60
Distinct (%)< 0.1%
Missing58623
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean4.774183294
Minimum0
Maximum71
Zeros23661
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.130526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile10
Maximum71
Range71
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.037921424
Coefficient of variation (CV)0.636322746
Kurtosis6.767222463
Mean4.774183294
Median Absolute Deviation (MAD)2
Skewness1.749103457
Sum10513124
Variance9.228966576
MonotonicityNot monotonic
2025-07-26T13:00:47.179495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 380903
16.8%
4 359451
15.9%
2 304911
13.5%
5 289607
12.8%
6 215249
9.5%
7 151218
 
6.7%
1 148636
 
6.6%
8 103846
 
4.6%
9 70471
 
3.1%
10 47462
 
2.1%
Other values (50) 130324
 
5.8%
(Missing) 58623
 
2.6%
ValueCountFrequency (%)
0 23661
 
1.0%
1 148636
 
6.6%
2 304911
13.5%
3 380903
16.8%
4 359451
15.9%
ValueCountFrequency (%)
71 1
< 0.1%
69 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
61 1
< 0.1%

num_bc_tl
Real number (ℝ)

Missing 

Distinct76
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean7.726401941
Minimum0
Maximum86
Zeros5701
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.229803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median7
Q310
95-th percentile17
Maximum86
Range86
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.701430113
Coefficient of variation (CV)0.6084889382
Kurtosis3.905674874
Mean7.726401941
Median Absolute Deviation (MAD)3
Skewness1.425565032
Sum16923849
Variance22.10344511
MonotonicityNot monotonic
2025-07-26T13:00:47.275392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 231341
10.2%
6 222963
9.9%
4 222258
9.8%
7 203775
9.0%
3 183556
8.1%
8 180553
8.0%
9 152474
 
6.7%
10 126591
 
5.6%
2 120417
 
5.3%
11 103389
 
4.6%
Other values (66) 443075
19.6%
ValueCountFrequency (%)
0 5701
 
0.3%
1 48219
 
2.1%
2 120417
5.3%
3 183556
8.1%
4 222258
9.8%
ValueCountFrequency (%)
86 1
< 0.1%
85 1
< 0.1%
82 1
< 0.1%
79 1
< 0.1%
77 1
< 0.1%

num_il_tl
Real number (ℝ)

Missing  Zeros 

Distinct122
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean8.413438782
Minimum0
Maximum159
Zeros68944
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.321918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q311
95-th percentile23
Maximum159
Range159
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.359113771
Coefficient of variation (CV)0.8746856026
Kurtosis7.841626515
Mean8.413438782
Median Absolute Deviation (MAD)3
Skewness2.103554166
Sum18428729
Variance54.15655549
MonotonicityNot monotonic
2025-07-26T13:00:47.375028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 190600
 
8.4%
3 190128
 
8.4%
5 180396
 
8.0%
2 173360
 
7.7%
6 165370
 
7.3%
7 147949
 
6.5%
1 133227
 
5.9%
8 129276
 
5.7%
9 111342
 
4.9%
10 96604
 
4.3%
Other values (112) 672140
29.7%
ValueCountFrequency (%)
0 68944
 
3.0%
1 133227
5.9%
2 173360
7.7%
3 190128
8.4%
4 190600
8.4%
ValueCountFrequency (%)
159 1
< 0.1%
150 1
< 0.1%
140 1
< 0.1%
138 1
< 0.1%
132 1
< 0.1%

num_op_rev_tl
Real number (ℝ)

Missing 

Distinct81
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean8.24652254
Minimum0
Maximum91
Zeros1113
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.423861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median7
Q310
95-th percentile17
Maximum91
Range91
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.683927892
Coefficient of variation (CV)0.5679882483
Kurtosis4.732174134
Mean8.24652254
Median Absolute Deviation (MAD)3
Skewness1.526702877
Sum18063117
Variance21.9391805
MonotonicityNot monotonic
2025-07-26T13:00:47.472279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 236912
10.5%
5 231071
10.2%
7 222688
9.9%
4 201987
8.9%
8 198673
8.8%
9 169770
 
7.5%
3 145954
 
6.5%
10 141418
 
6.3%
11 114837
 
5.1%
12 92236
 
4.1%
Other values (71) 434846
19.2%
ValueCountFrequency (%)
0 1113
 
< 0.1%
1 18563
 
0.8%
2 77763
 
3.4%
3 145954
6.5%
4 201987
8.9%
ValueCountFrequency (%)
91 3
< 0.1%
86 1
 
< 0.1%
83 1
 
< 0.1%
81 1
 
< 0.1%
79 1
 
< 0.1%

num_rev_accts
Real number (ℝ)

Missing 

Distinct117
Distinct (%)< 0.1%
Missing70310
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean14.00462977
Minimum0
Maximum151
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.516334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median12
Q318
95-th percentile29
Maximum151
Range151
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.038867538
Coefficient of variation (CV)0.5740149987
Kurtosis3.460986383
Mean14.00462977
Median Absolute Deviation (MAD)5
Skewness1.369541362
Sum30675615
Variance64.62339129
MonotonicityNot monotonic
2025-07-26T13:00:47.559998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 132715
 
5.9%
9 132157
 
5.8%
8 129674
 
5.7%
11 129189
 
5.7%
12 123608
 
5.5%
7 122058
 
5.4%
13 116460
 
5.2%
6 109695
 
4.9%
14 107620
 
4.8%
15 99544
 
4.4%
Other values (107) 987671
43.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 19
 
< 0.1%
2 21211
 
0.9%
3 44334
2.0%
4 69400
3.1%
ValueCountFrequency (%)
151 1
< 0.1%
143 1
< 0.1%
128 1
< 0.1%
127 2
< 0.1%
119 1
< 0.1%

num_rev_tl_bal_gt_0
Real number (ℝ)

Missing 

Distinct50
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean5.577950887
Minimum0
Maximum65
Zeros11252
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.602619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q37
95-th percentile12
Maximum65
Range65
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.293433856
Coefficient of variation (CV)0.5904379444
Kurtosis4.173233011
Mean5.577950887
Median Absolute Deviation (MAD)2
Skewness1.470687356
Sum12217899
Variance10.84670656
MonotonicityNot monotonic
2025-07-26T13:00:47.650325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 335837
14.9%
3 310629
13.7%
5 306205
13.5%
6 249901
11.1%
2 214144
9.5%
7 192557
8.5%
8 141264
6.2%
9 102127
 
4.5%
1 84778
 
3.8%
10 71574
 
3.2%
Other values (40) 181376
8.0%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 11252
 
0.5%
1 84778
 
3.8%
2 214144
9.5%
3 310629
13.7%
4 335837
14.9%
ValueCountFrequency (%)
65 1
 
< 0.1%
59 2
< 0.1%
55 1
 
< 0.1%
47 1
 
< 0.1%
45 3
< 0.1%

num_sats
Real number (ℝ)

Missing 

Distinct91
Distinct (%)< 0.1%
Missing58623
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean11.62812988
Minimum0
Maximum101
Zeros61
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.697088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q314
95-th percentile22
Maximum101
Range101
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.644026548
Coefficient of variation (CV)0.4853769784
Kurtosis3.421898901
Mean11.62812988
Median Absolute Deviation (MAD)3
Skewness1.314627806
Sum25606049
Variance31.85503568
MonotonicityNot monotonic
2025-07-26T13:00:47.743410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 190842
 
8.4%
10 185016
 
8.2%
8 183966
 
8.1%
11 170664
 
7.5%
7 168032
 
7.4%
12 153317
 
6.8%
6 141290
 
6.2%
13 133971
 
5.9%
14 115266
 
5.1%
5 105440
 
4.7%
Other values (81) 654274
28.9%
ValueCountFrequency (%)
0 61
 
< 0.1%
1 1634
 
0.1%
2 10216
 
0.5%
3 30948
1.4%
4 65577
2.9%
ValueCountFrequency (%)
101 1
< 0.1%
97 1
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
91 1
< 0.1%

num_tl_120dpd_2m
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing153690
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean0.0006373958181
Minimum0
Maximum7
Zeros2105738
Zeros (%)93.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.827239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.02710643324
Coefficient of variation (CV)42.52684513
Kurtosis5541.593233
Mean0.0006373958181
Median Absolute Deviation (MAD)0
Skewness55.80984712
Sum1343
Variance0.0007347587232
MonotonicityNot monotonic
2025-07-26T13:00:47.875703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 2105738
93.1%
1 1219
 
0.1%
2 46
 
< 0.1%
3 5
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
4 1
 
< 0.1%
(Missing) 153690
 
6.8%
ValueCountFrequency (%)
0 2105738
93.1%
1 1219
 
0.1%
2 46
 
< 0.1%
3 5
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 1
 
< 0.1%
4 1
 
< 0.1%
3 5
 
< 0.1%
2 46
< 0.1%

num_tl_30dpd
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct5
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean0.00281365162
Minimum0
Maximum4
Zeros2184561
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.903560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05616522447
Coefficient of variation (CV)19.96168398
Kurtosis622.4309597
Mean0.00281365162
Median Absolute Deviation (MAD)0
Skewness22.51746312
Sum6163
Variance0.00315453244
MonotonicityNot monotonic
2025-07-26T13:00:47.935167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 2184561
96.6%
1 5542
 
0.2%
2 253
 
< 0.1%
3 29
 
< 0.1%
4 7
 
< 0.1%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 2184561
96.6%
1 5542
 
0.2%
2 253
 
< 0.1%
3 29
 
< 0.1%
4 7
 
< 0.1%
ValueCountFrequency (%)
4 7
 
< 0.1%
3 29
 
< 0.1%
2 253
 
< 0.1%
1 5542
 
0.2%
0 2184561
96.6%

num_tl_90g_dpd_24m
Real number (ℝ)

Missing  Zeros 

Distinct34
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean0.08293766595
Minimum0
Maximum58
Zeros2073060
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:47.986309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4935732136
Coefficient of variation (CV)5.951134602
Kurtosis459.1979059
Mean0.08293766595
Median Absolute Deviation (MAD)0
Skewness14.90157353
Sum181666
Variance0.2436145172
MonotonicityNot monotonic
2025-07-26T13:00:48.036105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 2073060
91.7%
1 88450
 
3.9%
2 16897
 
0.7%
3 4672
 
0.2%
4 2653
 
0.1%
5 1398
 
0.1%
6 1018
 
< 0.1%
7 604
 
< 0.1%
8 467
 
< 0.1%
9 340
 
< 0.1%
Other values (24) 833
 
< 0.1%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 2073060
91.7%
1 88450
 
3.9%
2 16897
 
0.7%
3 4672
 
0.2%
4 2653
 
0.1%
ValueCountFrequency (%)
58 1
< 0.1%
42 1
< 0.1%
39 1
< 0.1%
36 1
< 0.1%
35 1
< 0.1%

num_tl_op_past_12m
Real number (ℝ)

Missing  Zeros 

Distinct33
Distinct (%)< 0.1%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean2.076755211
Minimum0
Maximum32
Zeros415975
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:48.078029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.830710765
Coefficient of variation (CV)0.8815245798
Kurtosis4.69604436
Mean2.076755211
Median Absolute Deviation (MAD)1
Skewness1.503461003
Sum4548908
Variance3.351501904
MonotonicityNot monotonic
2025-07-26T13:00:48.135353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 558928
24.7%
2 483541
21.4%
0 415975
18.4%
3 335448
14.8%
4 195095
 
8.6%
5 97421
 
4.3%
6 48518
 
2.1%
7 25934
 
1.1%
8 13066
 
0.6%
9 7131
 
0.3%
Other values (23) 9335
 
0.4%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 415975
18.4%
1 558928
24.7%
2 483541
21.4%
3 335448
14.8%
4 195095
 
8.6%
ValueCountFrequency (%)
32 1
 
< 0.1%
31 1
 
< 0.1%
30 2
< 0.1%
29 1
 
< 0.1%
28 4
< 0.1%

pct_tl_nvr_dlq
Real number (ℝ)

Missing 

Distinct690
Distinct (%)< 0.1%
Missing70464
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean94.11457646
Minimum0
Maximum100
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:48.192259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q191.3
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation9.036140361
Coefficient of variation (CV)0.09601212374
Kurtosis6.854075654
Mean94.11457646
Median Absolute Deviation (MAD)0
Skewness-2.277944445
Sum206133227.6
Variance81.65183263
MonotonicityNot monotonic
2025-07-26T13:00:48.298127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1106209
48.9%
90 29542
 
1.3%
95 27840
 
1.2%
96 24023
 
1.1%
91.7 23278
 
1.0%
90.9 23274
 
1.0%
92.3 22822
 
1.0%
88.9 22667
 
1.0%
87.5 22258
 
1.0%
92.9 22101
 
1.0%
Other values (680) 866223
38.3%
(Missing) 70464
 
3.1%
ValueCountFrequency (%)
0 13
< 0.1%
5 1
 
< 0.1%
5.9 1
 
< 0.1%
6.7 1
 
< 0.1%
7.1 2
 
< 0.1%
ValueCountFrequency (%)
100 1106209
48.9%
99.4 2
 
< 0.1%
99.3 1
 
< 0.1%
99.2 9
 
< 0.1%
99.1 15
 
< 0.1%

percent_bc_gt_75
Real number (ℝ)

Missing  Zeros 

Distinct284
Distinct (%)< 0.1%
Missing75412
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean42.43512654
Minimum0
Maximum100
Zeros598711
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:48.360933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.5
Q371.4
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)71.4

Descriptive statistics

Standard deviation36.21615733
Coefficient of variation (CV)0.85344761
Kurtosis-1.25565792
Mean42.43512654
Median Absolute Deviation (MAD)37.5
Skewness0.3091997107
Sum92733015.24
Variance1311.610051
MonotonicityNot monotonic
2025-07-26T13:00:48.413266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 598711
26.5%
100 372700
16.5%
50 231935
 
10.3%
33.3 147284
 
6.5%
66.7 134722
 
6.0%
25 97801
 
4.3%
75 76405
 
3.4%
20 64820
 
2.9%
40 55574
 
2.5%
60 47850
 
2.1%
Other values (274) 357487
15.8%
(Missing) 75412
 
3.3%
ValueCountFrequency (%)
0 598711
26.5%
0.14 2
 
< 0.1%
0.17 1
 
< 0.1%
0.2 10
 
< 0.1%
0.25 19
 
< 0.1%
ValueCountFrequency (%)
100 372700
16.5%
95.8 1
 
< 0.1%
95.5 2
 
< 0.1%
95.2 2
 
< 0.1%
95 3
 
< 0.1%

pub_rec_bankruptcies
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing1398
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.1281935181
Minimum0
Maximum12
Zeros1987383
Zeros (%)87.9%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:48.449034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3646129975
Coefficient of variation (CV)2.844238951
Kurtosis18.65787015
Mean0.1281935181
Median Absolute Deviation (MAD)0
Skewness3.37118635
Sum289628
Variance0.1329426379
MonotonicityNot monotonic
2025-07-26T13:00:48.483526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1987383
87.9%
1 258444
 
11.4%
2 10518
 
0.5%
3 2131
 
0.1%
4 541
 
< 0.1%
5 188
 
< 0.1%
6 60
 
< 0.1%
7 23
 
< 0.1%
8 10
 
< 0.1%
9 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 1398
 
0.1%
ValueCountFrequency (%)
0 1987383
87.9%
1 258444
 
11.4%
2 10518
 
0.5%
3 2131
 
0.1%
4 541
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 1
 
< 0.1%
9 3
 
< 0.1%
8 10
< 0.1%
7 23
< 0.1%

tax_liens
Real number (ℝ)

Skewed  Zeros 

Distinct42
Distinct (%)< 0.1%
Missing138
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.04677109198
Minimum0
Maximum85
Zeros2195933
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:48.523642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum85
Range85
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.377533821
Coefficient of variation (CV)8.071947971
Kurtosis3476.326736
Mean0.04677109198
Median Absolute Deviation (MAD)0
Skewness32.07091145
Sum105729
Variance0.142531786
MonotonicityNot monotonic
2025-07-26T13:00:48.567293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 2195933
97.1%
1 43638
 
1.9%
2 12172
 
0.5%
3 4456
 
0.2%
4 2007
 
0.1%
5 1026
 
< 0.1%
6 557
 
< 0.1%
7 265
 
< 0.1%
8 160
 
< 0.1%
9 103
 
< 0.1%
Other values (32) 246
 
< 0.1%
(Missing) 138
 
< 0.1%
ValueCountFrequency (%)
0 2195933
97.1%
1 43638
 
1.9%
2 12172
 
0.5%
3 4456
 
0.2%
4 2007
 
0.1%
ValueCountFrequency (%)
85 1
< 0.1%
63 1
< 0.1%
61 2
< 0.1%
53 1
< 0.1%
52 1
< 0.1%

tot_hi_cred_lim
Real number (ℝ)

Missing 

Distinct529972
Distinct (%)24.2%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean178242.7537
Minimum0
Maximum9999999
Zeros70
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:48.614383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18830
Q150731
median114298.5
Q3257755
95-th percentile510883.8
Maximum9999999
Range9999999
Interquartile range (IQR)207024

Descriptive statistics

Standard deviation181574.8147
Coefficient of variation (CV)1.018693949
Kurtosis84.33085943
Mean178242.7537
Median Absolute Deviation (MAD)79017.5
Skewness3.829997886
Sum3.904215019 × 1011
Variance3.296941332 × 1010
MonotonicityNot monotonic
2025-07-26T13:00:49.671972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12500 716
 
< 0.1%
15000 712
 
< 0.1%
15500 697
 
< 0.1%
19000 696
 
< 0.1%
13500 690
 
< 0.1%
16500 690
 
< 0.1%
16000 690
 
< 0.1%
11000 686
 
< 0.1%
14000 684
 
< 0.1%
13000 684
 
< 0.1%
Other values (529962) 2183447
96.6%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 70
< 0.1%
100 1
 
< 0.1%
119 1
 
< 0.1%
154 1
 
< 0.1%
200 16
 
< 0.1%
ValueCountFrequency (%)
9999999 14
< 0.1%
9792792 1
 
< 0.1%
9375662 1
 
< 0.1%
8700253 1
 
< 0.1%
8592561 1
 
< 0.1%

total_bal_ex_mort
Real number (ℝ)

Missing 

Distinct212777
Distinct (%)9.6%
Missing50063
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean51022.93846
Minimum0
Maximum3408095
Zeros1584
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:49.729498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6556.85
Q120892
median37864
Q364350
95-th percentile138768.15
Maximum3408095
Range3408095
Interquartile range (IQR)43458

Descriptive statistics

Standard deviation49911.23567
Coefficient of variation (CV)0.9782117058
Kurtosis65.58390119
Mean51022.93846
Median Absolute Deviation (MAD)19944
Skewness4.236560408
Sum1.127932466 × 1011
Variance2491131446
MonotonicityNot monotonic
2025-07-26T13:00:49.781681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1584
 
0.1%
23068 59
 
< 0.1%
24214 57
 
< 0.1%
20275 57
 
< 0.1%
19095 56
 
< 0.1%
20346 56
 
< 0.1%
25529 55
 
< 0.1%
22831 55
 
< 0.1%
20317 55
 
< 0.1%
23915 54
 
< 0.1%
Other values (212767) 2208550
97.7%
(Missing) 50063
 
2.2%
ValueCountFrequency (%)
0 1584
0.1%
1 23
 
< 0.1%
2 23
 
< 0.1%
3 27
 
< 0.1%
4 14
 
< 0.1%
ValueCountFrequency (%)
3408095 1
< 0.1%
2921551 1
< 0.1%
2698600 1
< 0.1%
2688920 1
< 0.1%
2652799 1
< 0.1%

total_bc_limit
Real number (ℝ)

Missing  Zeros 

Distinct20309
Distinct (%)0.9%
Missing50063
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean23193.76817
Minimum0
Maximum1569000
Zeros25349
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:49.838193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2300
Q18300
median16300
Q330300
95-th percentile67300
Maximum1569000
Range1569000
Interquartile range (IQR)22000

Descriptive statistics

Standard deviation23006.55824
Coefficient of variation (CV)0.9919284381
Kurtosis29.91804426
Mean23193.76817
Median Absolute Deviation (MAD)9600
Skewness2.990081245
Sum5.127302529 × 1010
Variance529301722
MonotonicityNot monotonic
2025-07-26T13:00:49.895824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25349
 
1.1%
5000 16941
 
0.7%
6000 15110
 
0.7%
10000 14744
 
0.7%
7000 14383
 
0.6%
8000 14055
 
0.6%
4000 13985
 
0.6%
3000 13939
 
0.6%
7500 13427
 
0.6%
9000 13175
 
0.6%
Other values (20299) 2055530
90.9%
(Missing) 50063
 
2.2%
ValueCountFrequency (%)
0 25349
1.1%
100 17
 
< 0.1%
200 260
 
< 0.1%
250 4
 
< 0.1%
251 1
 
< 0.1%
ValueCountFrequency (%)
1569000 1
< 0.1%
1105500 1
< 0.1%
1090700 1
< 0.1%
834300 1
< 0.1%
760000 1
< 0.1%

total_il_high_credit_limit
Real number (ℝ)

Missing  Zeros 

Distinct194137
Distinct (%)8.9%
Missing70309
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean43732.01348
Minimum0
Maximum2118996
Zeros263497
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:49.945544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115000
median32696
Q358804.25
95-th percentile125348.45
Maximum2118996
Range2118996
Interquartile range (IQR)43804.25

Descriptive statistics

Standard deviation45072.98219
Coefficient of variation (CV)1.03066332
Kurtosis28.28428368
Mean43732.01348
Median Absolute Deviation (MAD)20696
Skewness3.101032113
Sum9.579025246 × 1010
Variance2031573724
MonotonicityNot monotonic
2025-07-26T13:00:49.996475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 263497
 
11.7%
10000 13338
 
0.6%
15000 10273
 
0.5%
20000 8412
 
0.4%
5000 8160
 
0.4%
12000 6496
 
0.3%
25000 6200
 
0.3%
6000 5494
 
0.2%
8000 5136
 
0.2%
7000 3481
 
0.2%
Other values (194127) 1859905
82.3%
(Missing) 70309
 
3.1%
ValueCountFrequency (%)
0 263497
11.7%
36 1
 
< 0.1%
44 1
 
< 0.1%
59 1
 
< 0.1%
75 1
 
< 0.1%
ValueCountFrequency (%)
2118996 1
< 0.1%
2101913 1
< 0.1%
2000000 1
< 0.1%
1840000 1
< 0.1%
1736064 1
< 0.1%

revol_bal_joint
Real number (ℝ)

Missing 

Distinct56875
Distinct (%)52.7%
Missing2152681
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean33617.27885
Minimum0
Maximum1110019
Zeros106
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:50.083686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5358
Q115106.75
median26516.5
Q343769
95-th percentile85002.05
Maximum1110019
Range1110019
Interquartile range (IQR)28662.25

Descriptive statistics

Standard deviation28153.87431
Coefficient of variation (CV)0.8374822495
Kurtosis32.57097407
Mean33617.27885
Median Absolute Deviation (MAD)13296
Skewness3.024207091
Sum3631338461
Variance792640638.6
MonotonicityNot monotonic
2025-07-26T13:00:50.133886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 106
 
< 0.1%
20804 10
 
< 0.1%
26505 9
 
< 0.1%
22263 9
 
< 0.1%
12149 9
 
< 0.1%
19973 9
 
< 0.1%
20811 9
 
< 0.1%
10041 9
 
< 0.1%
21072 9
 
< 0.1%
15424 9
 
< 0.1%
Other values (56865) 107832
 
4.8%
(Missing) 2152681
95.2%
ValueCountFrequency (%)
0 106
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
1110019 1
< 0.1%
517755 1
< 0.1%
476826 1
< 0.1%
426860 1
< 0.1%
412216 1
< 0.1%

sec_app_fico_range_low
Real number (ℝ)

Missing 

Distinct62
Distinct (%)0.1%
Missing2152680
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean669.7556031
Minimum540
Maximum845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:50.185988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum540
5-th percentile595
Q1645
median670
Q3695
95-th percentile745
Maximum845
Range305
Interquartile range (IQR)50

Descriptive statistics

Standard deviation44.72916274
Coefficient of variation (CV)0.0667843054
Kurtosis0.5865937386
Mean669.7556031
Median Absolute Deviation (MAD)25
Skewness0.06325630177
Sum72347670
Variance2000.698
MonotonicityNot monotonic
2025-07-26T13:00:50.246417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
670 6015
 
0.3%
665 5759
 
0.3%
660 5708
 
0.3%
675 5499
 
0.2%
680 5255
 
0.2%
655 5107
 
0.2%
650 4832
 
0.2%
690 4757
 
0.2%
685 4618
 
0.2%
695 4305
 
0.2%
Other values (52) 56166
 
2.5%
(Missing) 2152680
95.2%
ValueCountFrequency (%)
540 262
< 0.1%
545 259
< 0.1%
550 300
< 0.1%
555 356
< 0.1%
560 381
< 0.1%
ValueCountFrequency (%)
845 3
 
< 0.1%
840 9
 
< 0.1%
835 10
 
< 0.1%
830 24
< 0.1%
825 41
< 0.1%

sec_app_fico_range_high
Real number (ℝ)

Missing 

Distinct62
Distinct (%)0.1%
Missing2152680
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean673.7556308
Minimum544
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:50.302573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum544
5-th percentile599
Q1649
median674
Q3699
95-th percentile749
Maximum850
Range306
Interquartile range (IQR)50

Descriptive statistics

Standard deviation44.72927186
Coefficient of variation (CV)0.06638797483
Kurtosis0.586709236
Mean673.7556308
Median Absolute Deviation (MAD)25
Skewness0.06328273252
Sum72779757
Variance2000.707761
MonotonicityNot monotonic
2025-07-26T13:00:50.353241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
674 6015
 
0.3%
669 5759
 
0.3%
664 5708
 
0.3%
679 5499
 
0.2%
684 5255
 
0.2%
659 5107
 
0.2%
654 4832
 
0.2%
694 4757
 
0.2%
689 4618
 
0.2%
699 4305
 
0.2%
Other values (52) 56166
 
2.5%
(Missing) 2152680
95.2%
ValueCountFrequency (%)
544 262
< 0.1%
549 259
< 0.1%
554 300
< 0.1%
559 356
< 0.1%
564 381
< 0.1%
ValueCountFrequency (%)
850 3
 
< 0.1%
844 9
 
< 0.1%
839 10
 
< 0.1%
834 24
< 0.1%
829 41
< 0.1%
Distinct663
Distinct (%)0.6%
Missing2152680
Missing (%)95.2%
Memory size71.6 MiB
2025-07-26T13:00:50.464195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters864168
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)< 0.1%

Sample

1st rowFeb-2005
2nd rowOct-1987
3rd rowFeb-2013
4th rowMar-2003
5th rowMay-2016
ValueCountFrequency (%)
aug-2006 998
 
0.9%
sep-2006 894
 
0.8%
aug-2005 894
 
0.8%
sep-2005 889
 
0.8%
sep-2004 848
 
0.8%
aug-2004 822
 
0.8%
oct-2005 816
 
0.8%
aug-2007 804
 
0.7%
jul-2006 768
 
0.7%
nov-2005 751
 
0.7%
Other values (653) 99537
92.1%
2025-07-26T13:00:50.684449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 148666
17.2%
- 108021
 
12.5%
2 87486
 
10.1%
9 61509
 
7.1%
1 55515
 
6.4%
u 28692
 
3.3%
e 26988
 
3.1%
J 25033
 
2.9%
a 24779
 
2.9%
A 19386
 
2.2%
Other values (23) 278093
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 864168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 148666
17.2%
- 108021
 
12.5%
2 87486
 
10.1%
9 61509
 
7.1%
1 55515
 
6.4%
u 28692
 
3.3%
e 26988
 
3.1%
J 25033
 
2.9%
a 24779
 
2.9%
A 19386
 
2.2%
Other values (23) 278093
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 864168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 148666
17.2%
- 108021
 
12.5%
2 87486
 
10.1%
9 61509
 
7.1%
1 55515
 
6.4%
u 28692
 
3.3%
e 26988
 
3.1%
J 25033
 
2.9%
a 24779
 
2.9%
A 19386
 
2.2%
Other values (23) 278093
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 864168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 148666
17.2%
- 108021
 
12.5%
2 87486
 
10.1%
9 61509
 
7.1%
1 55515
 
6.4%
u 28692
 
3.3%
e 26988
 
3.1%
J 25033
 
2.9%
a 24779
 
2.9%
A 19386
 
2.2%
Other values (23) 278093
32.2%

sec_app_inq_last_6mths
Real number (ℝ)

Missing  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing2152680
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean0.6332564964
Minimum0
Maximum6
Zeros65252
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:50.727181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9934011556
Coefficient of variation (CV)1.56871846
Kurtosis5.007834225
Mean0.6332564964
Median Absolute Deviation (MAD)0
Skewness2.04610811
Sum68405
Variance0.986845856
MonotonicityNot monotonic
2025-07-26T13:00:50.756483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 65252
 
2.9%
1 26964
 
1.2%
2 9698
 
0.4%
3 3643
 
0.2%
4 1509
 
0.1%
5 650
 
< 0.1%
6 305
 
< 0.1%
(Missing) 2152680
95.2%
ValueCountFrequency (%)
0 65252
2.9%
1 26964
1.2%
2 9698
 
0.4%
3 3643
 
0.2%
4 1509
 
0.1%
ValueCountFrequency (%)
6 305
 
< 0.1%
5 650
 
< 0.1%
4 1509
 
0.1%
3 3643
 
0.2%
2 9698
0.4%

sec_app_mort_acc
Real number (ℝ)

Missing  Zeros 

Distinct23
Distinct (%)< 0.1%
Missing2152680
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean1.538997047
Minimum0
Maximum27
Zeros42218
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:50.791438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum27
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.760568571
Coefficient of variation (CV)1.143971377
Kurtosis3.510018466
Mean1.538997047
Median Absolute Deviation (MAD)1
Skewness1.451643251
Sum166244
Variance3.099601694
MonotonicityNot monotonic
2025-07-26T13:00:50.834824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 42218
 
1.9%
1 21072
 
0.9%
2 17790
 
0.8%
3 12141
 
0.5%
4 7379
 
0.3%
5 3926
 
0.2%
6 1939
 
0.1%
7 851
 
< 0.1%
8 373
 
< 0.1%
9 165
 
< 0.1%
Other values (13) 167
 
< 0.1%
(Missing) 2152680
95.2%
ValueCountFrequency (%)
0 42218
1.9%
1 21072
0.9%
2 17790
0.8%
3 12141
 
0.5%
4 7379
 
0.3%
ValueCountFrequency (%)
27 1
< 0.1%
23 1
< 0.1%
22 1
< 0.1%
20 1
< 0.1%
18 2
< 0.1%

sec_app_open_acc
Real number (ℝ)

Missing 

Distinct67
Distinct (%)0.1%
Missing2152680
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean11.46945501
Minimum0
Maximum82
Zeros95
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:50.877848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median10
Q315
95-th percentile24
Maximum82
Range82
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.627271136
Coefficient of variation (CV)0.5778191839
Kurtosis2.577122093
Mean11.46945501
Median Absolute Deviation (MAD)4
Skewness1.187173558
Sum1238942
Variance43.92072271
MonotonicityNot monotonic
2025-07-26T13:00:50.924154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 7711
 
0.3%
8 7690
 
0.3%
7 7336
 
0.3%
10 7297
 
0.3%
11 6786
 
0.3%
6 6640
 
0.3%
12 6331
 
0.3%
5 5889
 
0.3%
13 5623
 
0.2%
14 5030
 
0.2%
Other values (57) 41688
 
1.8%
(Missing) 2152680
95.2%
ValueCountFrequency (%)
0 95
 
< 0.1%
1 1499
 
0.1%
2 2553
0.1%
3 3740
0.2%
4 4812
0.2%
ValueCountFrequency (%)
82 1
< 0.1%
73 1
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%

sec_app_revol_util
Real number (ℝ)

Missing 

Distinct1216
Distinct (%)1.1%
Missing2154517
Missing (%)95.3%
Infinite0
Infinite (%)0.0%
Mean58.16910081
Minimum0
Maximum434.3
Zeros1182
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:50.968894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.4
Q139.8
median60.2
Q378.6
95-th percentile95.9
Maximum434.3
Range434.3
Interquartile range (IQR)38.8

Descriptive statistics

Standard deviation25.54821161
Coefficient of variation (CV)0.4392058887
Kurtosis-0.1960062169
Mean58.16910081
Median Absolute Deviation (MAD)19.3
Skewness-0.2564452211
Sum6176627.8
Variance652.7111167
MonotonicityNot monotonic
2025-07-26T13:00:51.034022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1182
 
0.1%
59.4 180
 
< 0.1%
62.4 179
 
< 0.1%
67.2 174
 
< 0.1%
70.1 173
 
< 0.1%
77.9 172
 
< 0.1%
68.8 170
 
< 0.1%
73.5 170
 
< 0.1%
70.7 170
 
< 0.1%
69.8 168
 
< 0.1%
Other values (1206) 103446
 
4.6%
(Missing) 2154517
95.3%
ValueCountFrequency (%)
0 1182
0.1%
0.1 58
 
< 0.1%
0.2 36
 
< 0.1%
0.3 42
 
< 0.1%
0.4 33
 
< 0.1%
ValueCountFrequency (%)
434.3 1
< 0.1%
235.3 1
< 0.1%
212.6 1
< 0.1%
191 1
< 0.1%
184.2 1
< 0.1%

sec_app_open_act_il
Real number (ℝ)

Missing 

Distinct40
Distinct (%)< 0.1%
Missing2152680
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean3.010553503
Minimum0
Maximum43
Zeros14376
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:51.089216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9
Maximum43
Range43
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.275893064
Coefficient of variation (CV)1.088136471
Kurtosis11.81614557
Mean3.010553503
Median Absolute Deviation (MAD)1
Skewness2.800302479
Sum325203
Variance10.73147537
MonotonicityNot monotonic
2025-07-26T13:00:51.137574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 24089
 
1.1%
2 23099
 
1.0%
3 16430
 
0.7%
0 14376
 
0.6%
4 9942
 
0.4%
5 5905
 
0.3%
6 3593
 
0.2%
7 2361
 
0.1%
8 1684
 
0.1%
9 1307
 
0.1%
Other values (30) 5235
 
0.2%
(Missing) 2152680
95.2%
ValueCountFrequency (%)
0 14376
0.6%
1 24089
1.1%
2 23099
1.0%
3 16430
0.7%
4 9942
0.4%
ValueCountFrequency (%)
43 1
 
< 0.1%
39 4
< 0.1%
38 1
 
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%

sec_app_num_rev_accts
Real number (ℝ)

Missing 

Distinct86
Distinct (%)0.1%
Missing2152680
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean12.53307227
Minimum0
Maximum106
Zeros580
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:51.196675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median11
Q317
95-th percentile28
Maximum106
Range106
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.150963551
Coefficient of variation (CV)0.650356383
Kurtosis3.823558297
Mean12.53307227
Median Absolute Deviation (MAD)5
Skewness1.429661781
Sum1353835
Variance66.43820681
MonotonicityNot monotonic
2025-07-26T13:00:51.263103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 6639
 
0.3%
9 6543
 
0.3%
7 6473
 
0.3%
10 6282
 
0.3%
6 6221
 
0.3%
11 5880
 
0.3%
5 5619
 
0.2%
12 5569
 
0.2%
13 5178
 
0.2%
4 4922
 
0.2%
Other values (76) 48695
 
2.2%
(Missing) 2152680
95.2%
ValueCountFrequency (%)
0 580
 
< 0.1%
1 1741
 
0.1%
2 2730
0.1%
3 3822
0.2%
4 4922
0.2%
ValueCountFrequency (%)
106 1
< 0.1%
96 1
< 0.1%
95 1
< 0.1%
92 1
< 0.1%
90 1
< 0.1%

sec_app_chargeoff_within_12_mths
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct22
Distinct (%)< 0.1%
Missing2152680
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean0.0463520982
Minimum0
Maximum21
Zeros105117
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:51.306512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4114960112
Coefficient of variation (CV)8.877613467
Kurtosis640.9638779
Mean0.0463520982
Median Absolute Deviation (MAD)0
Skewness20.27699345
Sum5007
Variance0.1693289673
MonotonicityNot monotonic
2025-07-26T13:00:51.347665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 105117
 
4.6%
1 2073
 
0.1%
2 429
 
< 0.1%
3 146
 
< 0.1%
4 90
 
< 0.1%
5 57
 
< 0.1%
6 30
 
< 0.1%
7 20
 
< 0.1%
10 14
 
< 0.1%
8 12
 
< 0.1%
Other values (12) 33
 
< 0.1%
(Missing) 2152680
95.2%
ValueCountFrequency (%)
0 105117
4.6%
1 2073
 
0.1%
2 429
 
< 0.1%
3 146
 
< 0.1%
4 90
 
< 0.1%
ValueCountFrequency (%)
21 2
< 0.1%
20 2
< 0.1%
19 1
< 0.1%
18 2
< 0.1%
17 2
< 0.1%

sec_app_collections_12_mths_ex_med
Real number (ℝ)

Missing  Zeros 

Distinct18
Distinct (%)< 0.1%
Missing2152680
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean0.07756825062
Minimum0
Maximum23
Zeros101793
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:51.389805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4079956247
Coefficient of variation (CV)5.25982759
Kurtosis362.2128631
Mean0.07756825062
Median Absolute Deviation (MAD)0
Skewness13.30092154
Sum8379
Variance0.1664604298
MonotonicityNot monotonic
2025-07-26T13:00:51.427076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 101793
 
4.5%
1 5039
 
0.2%
2 799
 
< 0.1%
3 204
 
< 0.1%
4 79
 
< 0.1%
5 31
 
< 0.1%
6 25
 
< 0.1%
8 15
 
< 0.1%
10 11
 
< 0.1%
7 7
 
< 0.1%
Other values (8) 18
 
< 0.1%
(Missing) 2152680
95.2%
ValueCountFrequency (%)
0 101793
4.5%
1 5039
 
0.2%
2 799
 
< 0.1%
3 204
 
< 0.1%
4 79
 
< 0.1%
ValueCountFrequency (%)
23 1
< 0.1%
19 1
< 0.1%
18 1
< 0.1%
16 1
< 0.1%
15 2
< 0.1%

sec_app_mths_since_last_major_derog
Real number (ℝ)

Missing 

Distinct140
Distinct (%)0.4%
Missing2224759
Missing (%)98.4%
Infinite0
Infinite (%)0.0%
Mean36.93792777
Minimum0
Maximum185
Zeros399
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:51.481097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q116
median36
Q356
95-th percentile76
Maximum185
Range185
Interquartile range (IQR)40

Descriptive statistics

Standard deviation23.92458363
Coefficient of variation (CV)0.6476969629
Kurtosis-0.7008442253
Mean36.93792777
Median Absolute Deviation (MAD)20
Skewness0.28522913
Sum1327623
Variance572.385702
MonotonicityNot monotonic
2025-07-26T13:00:51.544194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1104
 
< 0.1%
2 633
 
< 0.1%
8 575
 
< 0.1%
5 552
 
< 0.1%
13 523
 
< 0.1%
9 522
 
< 0.1%
4 509
 
< 0.1%
15 509
 
< 0.1%
43 507
 
< 0.1%
14 504
 
< 0.1%
Other values (130) 30004
 
1.3%
(Missing) 2224759
98.4%
ValueCountFrequency (%)
0 399
 
< 0.1%
1 1104
< 0.1%
2 633
< 0.1%
3 484
< 0.1%
4 509
< 0.1%
ValueCountFrequency (%)
185 1
< 0.1%
159 1
< 0.1%
153 1
< 0.1%
147 1
< 0.1%
143 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size107.8 MiB
2025-07-26T13:00:51.583109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2260668
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN
ValueCountFrequency (%)
n 2259836
> 99.9%
y 832
 
< 0.1%
2025-07-26T13:00:51.645719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 2259836
> 99.9%
Y 832
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 2259836
> 99.9%
Y 832
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 2259836
> 99.9%
Y 832
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 2259836
> 99.9%
Y 832
 
< 0.1%

hardship_type
Text

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing2249784
Missing (%)99.5%
Memory size69.5 MiB
2025-07-26T13:00:51.688086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters338427
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINTEREST ONLY-3 MONTHS DEFERRAL
2nd rowINTEREST ONLY-3 MONTHS DEFERRAL
3rd rowINTEREST ONLY-3 MONTHS DEFERRAL
4th rowINTEREST ONLY-3 MONTHS DEFERRAL
5th rowINTEREST ONLY-3 MONTHS DEFERRAL
ValueCountFrequency (%)
interest 10917
25.0%
only-3 10917
25.0%
months 10917
25.0%
deferral 10917
25.0%
2025-07-26T13:00:51.767691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 43668
12.9%
T 32751
9.7%
R 32751
9.7%
32751
9.7%
N 32751
9.7%
L 21834
 
6.5%
S 21834
 
6.5%
O 21834
 
6.5%
M 10917
 
3.2%
F 10917
 
3.2%
Other values (7) 76419
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 338427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 43668
12.9%
T 32751
9.7%
R 32751
9.7%
32751
9.7%
N 32751
9.7%
L 21834
 
6.5%
S 21834
 
6.5%
O 21834
 
6.5%
M 10917
 
3.2%
F 10917
 
3.2%
Other values (7) 76419
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 338427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 43668
12.9%
T 32751
9.7%
R 32751
9.7%
32751
9.7%
N 32751
9.7%
L 21834
 
6.5%
S 21834
 
6.5%
O 21834
 
6.5%
M 10917
 
3.2%
F 10917
 
3.2%
Other values (7) 76419
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 338427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 43668
12.9%
T 32751
9.7%
R 32751
9.7%
32751
9.7%
N 32751
9.7%
L 21834
 
6.5%
S 21834
 
6.5%
O 21834
 
6.5%
M 10917
 
3.2%
F 10917
 
3.2%
Other values (7) 76419
22.6%

hardship_reason
Text

Missing 

Distinct9
Distinct (%)0.1%
Missing2249784
Missing (%)99.5%
Memory size69.3 MiB
2025-07-26T13:00:51.825059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length16
Mean length14.92497939
Min length7

Characters and Unicode

Total characters162936
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNATURAL_DISASTER
2nd rowNATURAL_DISASTER
3rd rowNATURAL_DISASTER
4th rowDIVORCE
5th rowNATURAL_DISASTER
ValueCountFrequency (%)
natural_disaster 2965
27.2%
excessive_obligations 2155
19.7%
unemployment 1923
17.6%
income_curtailment 1321
12.1%
medical 1294
11.9%
reduced_hours 662
 
6.1%
divorce 225
 
2.1%
family_death 214
 
2.0%
disability 158
 
1.4%
2025-07-26T13:00:51.920533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 18975
11.6%
I 14279
 
8.8%
A 14251
 
8.7%
S 13215
 
8.1%
T 13022
 
8.0%
N 11608
 
7.1%
L 10030
 
6.2%
R 8800
 
5.4%
O 8441
 
5.2%
M 7996
 
4.9%
Other values (12) 42319
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 162936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 18975
11.6%
I 14279
 
8.8%
A 14251
 
8.7%
S 13215
 
8.1%
T 13022
 
8.0%
N 11608
 
7.1%
L 10030
 
6.2%
R 8800
 
5.4%
O 8441
 
5.2%
M 7996
 
4.9%
Other values (12) 42319
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 162936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 18975
11.6%
I 14279
 
8.8%
A 14251
 
8.7%
S 13215
 
8.1%
T 13022
 
8.0%
N 11608
 
7.1%
L 10030
 
6.2%
R 8800
 
5.4%
O 8441
 
5.2%
M 7996
 
4.9%
Other values (12) 42319
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 162936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 18975
11.6%
I 14279
 
8.8%
A 14251
 
8.7%
S 13215
 
8.1%
T 13022
 
8.0%
N 11608
 
7.1%
L 10030
 
6.2%
R 8800
 
5.4%
O 8441
 
5.2%
M 7996
 
4.9%
Other values (12) 42319
26.0%

hardship_status
Text

Missing 

Distinct3
Distinct (%)< 0.1%
Missing2249784
Missing (%)99.5%
Memory size69.3 MiB
2025-07-26T13:00:51.962767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.148667216
Min length6

Characters and Unicode

Total characters88959
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBROKEN
2nd rowCOMPLETED
3rd rowCOMPLETED
4th rowCOMPLETED
5th rowCOMPLETED
ValueCountFrequency (%)
completed 7819
71.6%
broken 2266
 
20.8%
active 832
 
7.6%
2025-07-26T13:00:52.043956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 18736
21.1%
O 10085
11.3%
C 8651
9.7%
T 8651
9.7%
M 7819
8.8%
P 7819
8.8%
L 7819
8.8%
D 7819
8.8%
B 2266
 
2.5%
R 2266
 
2.5%
Other values (5) 7028
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 88959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 18736
21.1%
O 10085
11.3%
C 8651
9.7%
T 8651
9.7%
M 7819
8.8%
P 7819
8.8%
L 7819
8.8%
D 7819
8.8%
B 2266
 
2.5%
R 2266
 
2.5%
Other values (5) 7028
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 88959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 18736
21.1%
O 10085
11.3%
C 8651
9.7%
T 8651
9.7%
M 7819
8.8%
P 7819
8.8%
L 7819
8.8%
D 7819
8.8%
B 2266
 
2.5%
R 2266
 
2.5%
Other values (5) 7028
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 88959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 18736
21.1%
O 10085
11.3%
C 8651
9.7%
T 8651
9.7%
M 7819
8.8%
P 7819
8.8%
L 7819
8.8%
D 7819
8.8%
B 2266
 
2.5%
R 2266
 
2.5%
Other values (5) 7028
 
7.9%

deferral_term
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing2249784
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean3
Minimum3
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:52.078259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median3
Q33
95-th percentile3
Maximum3
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean3
Median Absolute Deviation (MAD)0
Skewness0
Sum32751
Variance0
MonotonicityIncreasing
2025-07-26T13:00:52.106583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
3 10917
 
0.5%
(Missing) 2249784
99.5%
ValueCountFrequency (%)
3 10917
0.5%
ValueCountFrequency (%)
3 10917
0.5%

hardship_amount
Real number (ℝ)

Missing 

Distinct9162
Distinct (%)83.9%
Missing2249784
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean155.0459806
Minimum0.64
Maximum943.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:52.148584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.64
5-th percentile21.068
Q159.44
median119.14
Q3213.26
95-th percentile411.056
Maximum943.94
Range943.3
Interquartile range (IQR)153.82

Descriptive statistics

Standard deviation129.0405941
Coefficient of variation (CV)0.8322730688
Kurtosis3.256810044
Mean155.0459806
Median Absolute Deviation (MAD)69.06
Skewness1.59871819
Sum1692636.97
Variance16651.47492
MonotonicityNot monotonic
2025-07-26T13:00:52.198655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132.33 5
 
< 0.1%
48.56 5
 
< 0.1%
53.05 5
 
< 0.1%
94.59 5
 
< 0.1%
69.9 5
 
< 0.1%
58 4
 
< 0.1%
45.06 4
 
< 0.1%
156.37 4
 
< 0.1%
75.36 4
 
< 0.1%
150.88 4
 
< 0.1%
Other values (9152) 10872
 
0.5%
(Missing) 2249784
99.5%
ValueCountFrequency (%)
0.64 1
< 0.1%
1.47 1
< 0.1%
1.61 1
< 0.1%
2.02 1
< 0.1%
2.15 1
< 0.1%
ValueCountFrequency (%)
943.94 1
< 0.1%
923.4 1
< 0.1%
893.63 1
< 0.1%
893.05 1
< 0.1%
845.22 1
< 0.1%

hardship_start_date
Text

Missing 

Distinct27
Distinct (%)0.2%
Missing2249784
Missing (%)99.5%
Memory size69.3 MiB
2025-07-26T13:00:52.256825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters87336
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSep-2017
2nd rowSep-2017
3rd rowSep-2017
4th rowJul-2017
5th rowSep-2018
ValueCountFrequency (%)
sep-2017 2444
22.4%
oct-2017 1077
 
9.9%
oct-2018 594
 
5.4%
nov-2017 466
 
4.3%
aug-2018 463
 
4.2%
jan-2019 431
 
3.9%
sep-2018 422
 
3.9%
nov-2018 420
 
3.8%
jun-2017 400
 
3.7%
may-2017 373
 
3.4%
Other values (17) 3827
35.1%
2025-07-26T13:00:52.352635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 5779
 
6.6%
e 4114
 
4.7%
8 4106
 
4.7%
p 3080
 
3.5%
S 2866
 
3.3%
c 2369
 
2.7%
Other values (19) 21354
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 5779
 
6.6%
e 4114
 
4.7%
8 4106
 
4.7%
p 3080
 
3.5%
S 2866
 
3.3%
c 2369
 
2.7%
Other values (19) 21354
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 5779
 
6.6%
e 4114
 
4.7%
8 4106
 
4.7%
p 3080
 
3.5%
S 2866
 
3.3%
c 2369
 
2.7%
Other values (19) 21354
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 5779
 
6.6%
e 4114
 
4.7%
8 4106
 
4.7%
p 3080
 
3.5%
S 2866
 
3.3%
c 2369
 
2.7%
Other values (19) 21354
24.5%

hardship_end_date
Text

Missing 

Distinct28
Distinct (%)0.3%
Missing2249784
Missing (%)99.5%
Memory size69.3 MiB
2025-07-26T13:00:52.416849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters87336
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDec-2017
2nd rowNov-2017
3rd rowDec-2017
4th rowOct-2017
5th rowDec-2018
ValueCountFrequency (%)
dec-2017 1756
16.1%
nov-2017 1325
 
12.1%
jan-2018 749
 
6.9%
jan-2019 517
 
4.7%
dec-2018 509
 
4.7%
mar-2019 468
 
4.3%
nov-2018 413
 
3.8%
feb-2018 401
 
3.7%
oct-2018 397
 
3.6%
oct-2017 396
 
3.6%
Other values (18) 3986
36.5%
2025-07-26T13:00:52.511966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 4481
 
5.1%
8 4360
 
5.0%
e 3700
 
4.2%
c 3058
 
3.5%
a 2650
 
3.0%
D 2265
 
2.6%
Other values (19) 23154
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 4481
 
5.1%
8 4360
 
5.0%
e 3700
 
4.2%
c 3058
 
3.5%
a 2650
 
3.0%
D 2265
 
2.6%
Other values (19) 23154
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 4481
 
5.1%
8 4360
 
5.0%
e 3700
 
4.2%
c 3058
 
3.5%
a 2650
 
3.0%
D 2265
 
2.6%
Other values (19) 23154
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 4481
 
5.1%
8 4360
 
5.0%
e 3700
 
4.2%
c 3058
 
3.5%
a 2650
 
3.0%
D 2265
 
2.6%
Other values (19) 23154
26.5%
Distinct27
Distinct (%)0.2%
Missing2249784
Missing (%)99.5%
Memory size69.3 MiB
2025-07-26T13:00:52.574560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters87336
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOct-2017
2nd rowSep-2017
3rd rowSep-2017
4th rowAug-2017
5th rowSep-2018
ValueCountFrequency (%)
sep-2017 1715
15.7%
oct-2017 1629
14.9%
nov-2017 640
 
5.9%
oct-2018 538
 
4.9%
nov-2018 481
 
4.4%
aug-2018 456
 
4.2%
sep-2018 416
 
3.8%
dec-2017 413
 
3.8%
jun-2017 394
 
3.6%
feb-2019 387
 
3.5%
Other values (17) 3848
35.2%
2025-07-26T13:00:52.666982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 5670
 
6.5%
8 4086
 
4.7%
e 3626
 
4.2%
c 2959
 
3.4%
p 2458
 
2.8%
O 2167
 
2.5%
Other values (19) 22702
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 5670
 
6.5%
8 4086
 
4.7%
e 3626
 
4.2%
c 2959
 
3.4%
p 2458
 
2.8%
O 2167
 
2.5%
Other values (19) 22702
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 5670
 
6.5%
8 4086
 
4.7%
e 3626
 
4.2%
c 2959
 
3.4%
p 2458
 
2.8%
O 2167
 
2.5%
Other values (19) 22702
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 10917
12.5%
2 10917
12.5%
0 10917
12.5%
1 10917
12.5%
7 5670
 
6.5%
8 4086
 
4.7%
e 3626
 
4.2%
c 2959
 
3.4%
p 2458
 
2.8%
O 2167
 
2.5%
Other values (19) 22702
26.0%

hardship_length
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing2249784
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean3
Minimum3
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:52.701134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median3
Q33
95-th percentile3
Maximum3
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean3
Median Absolute Deviation (MAD)0
Skewness0
Sum32751
Variance0
MonotonicityIncreasing
2025-07-26T13:00:52.726941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
3 10917
 
0.5%
(Missing) 2249784
99.5%
ValueCountFrequency (%)
3 10917
0.5%
ValueCountFrequency (%)
3 10917
0.5%

hardship_dpd
Real number (ℝ)

Missing 

Distinct34
Distinct (%)0.3%
Missing2249784
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean13.74388568
Minimum0
Maximum37
Zeros2402
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:52.760657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median15
Q322
95-th percentile28
Maximum37
Range37
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.671178203
Coefficient of variation (CV)0.7036713216
Kurtosis-1.298887335
Mean13.74388568
Median Absolute Deviation (MAD)8
Skewness-0.132871902
Sum150042
Variance93.53168783
MonotonicityNot monotonic
2025-07-26T13:00:52.801537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 2402
 
0.1%
26 425
 
< 0.1%
23 423
 
< 0.1%
25 419
 
< 0.1%
20 394
 
< 0.1%
16 386
 
< 0.1%
11 381
 
< 0.1%
22 375
 
< 0.1%
17 372
 
< 0.1%
27 363
 
< 0.1%
Other values (24) 4977
 
0.2%
(Missing) 2249784
99.5%
ValueCountFrequency (%)
0 2402
0.1%
1 47
 
< 0.1%
2 53
 
< 0.1%
3 67
 
< 0.1%
4 84
 
< 0.1%
ValueCountFrequency (%)
37 1
 
< 0.1%
32 4
 
< 0.1%
31 2
 
< 0.1%
30 29
 
< 0.1%
29 268
< 0.1%

hardship_loan_status
Text

Missing 

Distinct5
Distinct (%)< 0.1%
Missing2249784
Missing (%)99.5%
Memory size69.3 MiB
2025-07-26T13:00:52.852711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length13.95630668
Min length6

Characters and Unicode

Total characters152361
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLate (16-30 days)
2nd rowCurrent
3rd rowLate (16-30 days)
4th rowLate (16-30 days)
5th rowCurrent
ValueCountFrequency (%)
late 5221
19.2%
days 5221
19.2%
16-30 4770
17.5%
in 2912
10.7%
grace 2912
10.7%
period 2912
10.7%
current 2769
10.2%
31-120 451
 
1.7%
issued 15
 
0.1%
2025-07-26T13:00:52.937378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16266
 
10.7%
e 13829
 
9.1%
a 13354
 
8.8%
r 11362
 
7.5%
d 8148
 
5.3%
t 7990
 
5.2%
n 5681
 
3.7%
1 5672
 
3.7%
s 5251
 
3.4%
) 5221
 
3.4%
Other values (16) 59587
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16266
 
10.7%
e 13829
 
9.1%
a 13354
 
8.8%
r 11362
 
7.5%
d 8148
 
5.3%
t 7990
 
5.2%
n 5681
 
3.7%
1 5672
 
3.7%
s 5251
 
3.4%
) 5221
 
3.4%
Other values (16) 59587
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16266
 
10.7%
e 13829
 
9.1%
a 13354
 
8.8%
r 11362
 
7.5%
d 8148
 
5.3%
t 7990
 
5.2%
n 5681
 
3.7%
1 5672
 
3.7%
s 5251
 
3.4%
) 5221
 
3.4%
Other values (16) 59587
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16266
 
10.7%
e 13829
 
9.1%
a 13354
 
8.8%
r 11362
 
7.5%
d 8148
 
5.3%
t 7990
 
5.2%
n 5681
 
3.7%
1 5672
 
3.7%
s 5251
 
3.4%
) 5221
 
3.4%
Other values (16) 59587
39.1%
Distinct7487
Distinct (%)86.5%
Missing2252050
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean454.7980892
Minimum1.92
Maximum2680.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:52.980055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.92
5-th percentile62.13
Q1175.23
median352.77
Q3620.175
95-th percentile1189.005
Maximum2680.89
Range2678.97
Interquartile range (IQR)444.945

Descriptive statistics

Standard deviation375.3855001
Coefficient of variation (CV)0.8253893518
Kurtosis3.248800957
Mean454.7980892
Median Absolute Deviation (MAD)202.2
Skewness1.588220317
Sum3934458.27
Variance140914.2737
MonotonicityNot monotonic
2025-07-26T13:00:53.021435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.99 5
 
< 0.1%
283.77 5
 
< 0.1%
209.7 4
 
< 0.1%
163.65 4
 
< 0.1%
200.37 4
 
< 0.1%
237.78 4
 
< 0.1%
584.46 4
 
< 0.1%
268.14 4
 
< 0.1%
154.77 4
 
< 0.1%
186.9 4
 
< 0.1%
Other values (7477) 8609
 
0.4%
(Missing) 2252050
99.6%
ValueCountFrequency (%)
1.92 1
< 0.1%
4.41 1
< 0.1%
6.06 1
< 0.1%
6.45 1
< 0.1%
10.17 1
< 0.1%
ValueCountFrequency (%)
2680.89 1
< 0.1%
2679.15 1
< 0.1%
2535.66 1
< 0.1%
2513.04 1
< 0.1%
2486.94 1
< 0.1%

hardship_payoff_balance_amount
Real number (ℝ)

Missing 

Distinct10893
Distinct (%)99.8%
Missing2249784
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean11636.88394
Minimum55.73
Maximum40306.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:53.093195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum55.73
5-th percentile2183.706
Q15627
median10028.39
Q316151.89
95-th percentile26860.882
Maximum40306.41
Range40250.68
Interquartile range (IQR)10524.89

Descriptive statistics

Standard deviation7625.988281
Coefficient of variation (CV)0.6553290657
Kurtosis0.1998384506
Mean11636.88394
Median Absolute Deviation (MAD)4995.9
Skewness0.8645866635
Sum127039862
Variance58155697.26
MonotonicityNot monotonic
2025-07-26T13:00:53.144439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15557.86 2
 
< 0.1%
8237.68 2
 
< 0.1%
9599.86 2
 
< 0.1%
6524.54 2
 
< 0.1%
5131.62 2
 
< 0.1%
8773.97 2
 
< 0.1%
2825 2
 
< 0.1%
5262.81 2
 
< 0.1%
10747.75 2
 
< 0.1%
6627.94 2
 
< 0.1%
Other values (10883) 10897
 
0.5%
(Missing) 2249784
99.5%
ValueCountFrequency (%)
55.73 1
< 0.1%
174.15 1
< 0.1%
191.12 1
< 0.1%
193.98 1
< 0.1%
206.97 1
< 0.1%
ValueCountFrequency (%)
40306.41 1
< 0.1%
40149.35 1
< 0.1%
39746.94 1
< 0.1%
39542.45 1
< 0.1%
38824.41 1
< 0.1%

hardship_last_payment_amount
Real number (ℝ)

Missing 

Distinct9045
Distinct (%)82.9%
Missing2249784
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean193.9943208
Minimum0.01
Maximum1407.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:53.198872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.37
Q144.44
median133.16
Q3284.19
95-th percentile596.326
Maximum1407.86
Range1407.85
Interquartile range (IQR)239.75

Descriptive statistics

Standard deviation198.6294958
Coefficient of variation (CV)1.023893354
Kurtosis3.251575683
Mean193.9943208
Median Absolute Deviation (MAD)105.18
Skewness1.626587167
Sum2117836
Variance39453.67661
MonotonicityNot monotonic
2025-07-26T13:00:53.267810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 25
 
< 0.1%
0.03 25
 
< 0.1%
0.11 23
 
< 0.1%
0.12 23
 
< 0.1%
0.05 21
 
< 0.1%
0.1 21
 
< 0.1%
0.04 20
 
< 0.1%
0.07 18
 
< 0.1%
0.09 18
 
< 0.1%
0.17 17
 
< 0.1%
Other values (9035) 10706
 
0.5%
(Missing) 2249784
99.5%
ValueCountFrequency (%)
0.01 16
< 0.1%
0.02 25
< 0.1%
0.03 25
< 0.1%
0.04 20
< 0.1%
0.05 21
< 0.1%
ValueCountFrequency (%)
1407.86 1
< 0.1%
1377.17 1
< 0.1%
1291.21 1
< 0.1%
1290.59 1
< 0.1%
1283.9 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size114.6 MiB
2025-07-26T13:00:53.310373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length4
Mean length4.172785212
Min length4

Characters and Unicode

Total characters9433282
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCash
2nd rowCash
3rd rowCash
4th rowCash
5th rowCash
ValueCountFrequency (%)
cash 2182546
96.5%
directpay 78122
 
3.5%
2025-07-26T13:00:53.387457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2260668
24.0%
C 2182546
23.1%
s 2182546
23.1%
h 2182546
23.1%
D 78122
 
0.8%
i 78122
 
0.8%
r 78122
 
0.8%
e 78122
 
0.8%
c 78122
 
0.8%
t 78122
 
0.8%
Other values (2) 156244
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9433282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2260668
24.0%
C 2182546
23.1%
s 2182546
23.1%
h 2182546
23.1%
D 78122
 
0.8%
i 78122
 
0.8%
r 78122
 
0.8%
e 78122
 
0.8%
c 78122
 
0.8%
t 78122
 
0.8%
Other values (2) 156244
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9433282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2260668
24.0%
C 2182546
23.1%
s 2182546
23.1%
h 2182546
23.1%
D 78122
 
0.8%
i 78122
 
0.8%
r 78122
 
0.8%
e 78122
 
0.8%
c 78122
 
0.8%
t 78122
 
0.8%
Other values (2) 156244
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9433282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2260668
24.0%
C 2182546
23.1%
s 2182546
23.1%
h 2182546
23.1%
D 78122
 
0.8%
i 78122
 
0.8%
r 78122
 
0.8%
e 78122
 
0.8%
c 78122
 
0.8%
t 78122
 
0.8%
Other values (2) 156244
 
1.7%
Distinct2
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Memory size107.8 MiB
2025-07-26T13:00:53.416332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2260668
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN
ValueCountFrequency (%)
n 2226422
98.5%
y 34246
 
1.5%
2025-07-26T13:00:53.487139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 2226422
98.5%
Y 34246
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 2226422
98.5%
Y 34246
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 2226422
98.5%
Y 34246
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2260668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 2226422
98.5%
Y 34246
 
1.5%
Distinct83
Distinct (%)0.2%
Missing2226455
Missing (%)98.5%
Memory size69.8 MiB
2025-07-26T13:00:53.555276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters273968
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowNov-2017
2nd rowMay-2018
3rd rowFeb-2019
4th rowMay-2018
5th rowNov-2018
ValueCountFrequency (%)
feb-2019 2606
 
7.6%
mar-2019 2528
 
7.4%
jan-2019 2524
 
7.4%
oct-2018 2324
 
6.8%
dec-2018 2252
 
6.6%
nov-2018 2186
 
6.4%
aug-2018 1978
 
5.8%
jun-2018 1864
 
5.4%
jul-2018 1530
 
4.5%
sep-2018 1524
 
4.5%
Other values (73) 12930
37.8%
2025-07-26T13:00:53.748400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 34258
12.5%
1 34249
12.5%
0 34247
12.5%
- 34246
12.5%
8 19269
 
7.0%
a 9840
 
3.6%
e 9597
 
3.5%
J 8902
 
3.2%
9 7658
 
2.8%
u 7334
 
2.7%
Other values (23) 74368
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 273968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 34258
12.5%
1 34249
12.5%
0 34247
12.5%
- 34246
12.5%
8 19269
 
7.0%
a 9840
 
3.6%
e 9597
 
3.5%
J 8902
 
3.2%
9 7658
 
2.8%
u 7334
 
2.7%
Other values (23) 74368
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 273968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 34258
12.5%
1 34249
12.5%
0 34247
12.5%
- 34246
12.5%
8 19269
 
7.0%
a 9840
 
3.6%
e 9597
 
3.5%
J 8902
 
3.2%
9 7658
 
2.8%
u 7334
 
2.7%
Other values (23) 74368
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 273968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 34258
12.5%
1 34249
12.5%
0 34247
12.5%
- 34246
12.5%
8 19269
 
7.0%
a 9840
 
3.6%
e 9597
 
3.5%
J 8902
 
3.2%
9 7658
 
2.8%
u 7334
 
2.7%
Other values (23) 74368
27.1%

settlement_status
Text

Missing 

Distinct3
Distinct (%)< 0.1%
Missing2226455
Missing (%)98.5%
Memory size69.8 MiB
2025-07-26T13:00:53.801638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.847106231
Min length6

Characters and Unicode

Total characters234486
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMPLETE
2nd rowBROKEN
3rd rowCOMPLETE
4th rowACTIVE
5th rowCOMPLETE
ValueCountFrequency (%)
active 14704
42.9%
complete 14505
42.4%
broken 5037
 
14.7%
2025-07-26T13:00:53.888807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 48751
20.8%
C 29209
12.5%
T 29209
12.5%
O 19542
8.3%
A 14704
 
6.3%
I 14704
 
6.3%
V 14704
 
6.3%
M 14505
 
6.2%
P 14505
 
6.2%
L 14505
 
6.2%
Other values (4) 20148
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 234486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 48751
20.8%
C 29209
12.5%
T 29209
12.5%
O 19542
8.3%
A 14704
 
6.3%
I 14704
 
6.3%
V 14704
 
6.3%
M 14505
 
6.2%
P 14505
 
6.2%
L 14505
 
6.2%
Other values (4) 20148
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 234486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 48751
20.8%
C 29209
12.5%
T 29209
12.5%
O 19542
8.3%
A 14704
 
6.3%
I 14704
 
6.3%
V 14704
 
6.3%
M 14505
 
6.2%
P 14505
 
6.2%
L 14505
 
6.2%
Other values (4) 20148
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 234486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 48751
20.8%
C 29209
12.5%
T 29209
12.5%
O 19542
8.3%
A 14704
 
6.3%
I 14704
 
6.3%
V 14704
 
6.3%
M 14505
 
6.2%
P 14505
 
6.2%
L 14505
 
6.2%
Other values (4) 20148
8.6%

settlement_date
Text

Missing 

Distinct90
Distinct (%)0.3%
Missing2226455
Missing (%)98.5%
Memory size69.8 MiB
2025-07-26T13:00:53.980499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters273968
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowSep-2017
2nd rowNov-2017
3rd rowJan-2018
4th rowMay-2018
5th rowSep-2017
ValueCountFrequency (%)
jan-2019 1710
 
5.0%
oct-2018 1526
 
4.5%
feb-2019 1424
 
4.2%
mar-2018 1406
 
4.1%
sep-2018 1404
 
4.1%
nov-2018 1397
 
4.1%
jun-2018 1395
 
4.1%
jan-2018 1390
 
4.1%
aug-2018 1384
 
4.0%
may-2018 1357
 
4.0%
Other values (80) 19853
58.0%
2025-07-26T13:00:54.121659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 34272
12.5%
1 34250
12.5%
0 34248
12.5%
- 34246
12.5%
8 16485
 
6.0%
7 10215
 
3.7%
a 9083
 
3.3%
e 8982
 
3.3%
J 8616
 
3.1%
u 7522
 
2.7%
Other values (23) 76049
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 273968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 34272
12.5%
1 34250
12.5%
0 34248
12.5%
- 34246
12.5%
8 16485
 
6.0%
7 10215
 
3.7%
a 9083
 
3.3%
e 8982
 
3.3%
J 8616
 
3.1%
u 7522
 
2.7%
Other values (23) 76049
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 273968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 34272
12.5%
1 34250
12.5%
0 34248
12.5%
- 34246
12.5%
8 16485
 
6.0%
7 10215
 
3.7%
a 9083
 
3.3%
e 8982
 
3.3%
J 8616
 
3.1%
u 7522
 
2.7%
Other values (23) 76049
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 273968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 34272
12.5%
1 34250
12.5%
0 34248
12.5%
- 34246
12.5%
8 16485
 
6.0%
7 10215
 
3.7%
a 9083
 
3.3%
e 8982
 
3.3%
J 8616
 
3.1%
u 7522
 
2.7%
Other values (23) 76049
27.8%

settlement_amount
Real number (ℝ)

Missing 

Distinct21941
Distinct (%)64.1%
Missing2226455
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean5010.664267
Minimum44.21
Maximum33601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:54.181301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum44.21
5-th percentile780
Q12208
median4146.11
Q36850.1725
95-th percentile12351.3025
Maximum33601
Range33556.79
Interquartile range (IQR)4642.1725

Descriptive statistics

Standard deviation3693.12259
Coefficient of variation (CV)0.7370524931
Kurtosis2.176044134
Mean5010.664267
Median Absolute Deviation (MAD)2203.89
Skewness1.311380368
Sum171595208.5
Variance13639154.47
MonotonicityNot monotonic
2025-07-26T13:00:54.240827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 65
 
< 0.1%
4000 52
 
< 0.1%
3000 50
 
< 0.1%
6000 50
 
< 0.1%
10000 46
 
< 0.1%
8000 45
 
< 0.1%
7000 41
 
< 0.1%
3500 39
 
< 0.1%
6500 37
 
< 0.1%
9000 35
 
< 0.1%
Other values (21931) 33786
 
1.5%
(Missing) 2226455
98.5%
ValueCountFrequency (%)
44.21 1
< 0.1%
60.84 1
< 0.1%
82.96 1
< 0.1%
107 1
< 0.1%
120 1
< 0.1%
ValueCountFrequency (%)
33601 1
< 0.1%
30000 1
< 0.1%
28503 1
< 0.1%
28000 1
< 0.1%
27850 1
< 0.1%

settlement_percentage
Real number (ℝ)

Missing 

Distinct2070
Distinct (%)6.0%
Missing2226455
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean47.78036501
Minimum0.2
Maximum521.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:54.316211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile40
Q145
median45
Q350
95-th percentile60.01
Maximum521.35
Range521.15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.311821706
Coefficient of variation (CV)0.153029842
Kurtosis522.6570965
Mean47.78036501
Median Absolute Deviation (MAD)4.85
Skewness9.117278087
Sum1636286.38
Variance53.46273666
MonotonicityNot monotonic
2025-07-26T13:00:54.372555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 11919
 
0.5%
50 5698
 
0.3%
40 2212
 
0.1%
45.01 1679
 
0.1%
60 1310
 
0.1%
55 1143
 
0.1%
50.01 1036
 
< 0.1%
44.99 869
 
< 0.1%
65 686
 
< 0.1%
49.99 484
 
< 0.1%
Other values (2060) 7210
 
0.3%
(Missing) 2226455
98.5%
ValueCountFrequency (%)
0.2 1
< 0.1%
0.45 1
< 0.1%
0.55 1
< 0.1%
0.65 1
< 0.1%
10.69 1
< 0.1%
ValueCountFrequency (%)
521.35 1
 
< 0.1%
184.36 1
 
< 0.1%
166.67 1
 
< 0.1%
100 3
< 0.1%
98.57 1
 
< 0.1%

settlement_term
Real number (ℝ)

Missing 

Distinct40
Distinct (%)0.1%
Missing2226455
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean13.19132161
Minimum0
Maximum181
Zeros2727
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2025-07-26T13:00:54.424542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median14
Q318
95-th percentile24
Maximum181
Range181
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.159979582
Coefficient of variation (CV)0.6185869635
Kurtosis5.645983917
Mean13.19132161
Median Absolute Deviation (MAD)6
Skewness0.1679064306
Sum451750
Variance66.58526678
MonotonicityNot monotonic
2025-07-26T13:00:54.467286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
18 6977
 
0.3%
24 6387
 
0.3%
12 4830
 
0.2%
1 3141
 
0.1%
0 2727
 
0.1%
6 1979
 
0.1%
16 1427
 
0.1%
10 1196
 
0.1%
8 1091
 
< 0.1%
14 963
 
< 0.1%
Other values (30) 3528
 
0.2%
(Missing) 2226455
98.5%
ValueCountFrequency (%)
0 2727
0.1%
1 3141
0.1%
2 282
 
< 0.1%
3 247
 
< 0.1%
4 533
 
< 0.1%
ValueCountFrequency (%)
181 1
 
< 0.1%
118 1
 
< 0.1%
112 1
 
< 0.1%
65 3
< 0.1%
60 1
 
< 0.1%